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Title

タイ生命保険事業における顧客感情ナレッジマネ
ジメント

Author(s)SUCHARITTHAM, Nanthawadee

CitationIssue Date

2021‑03

Type

Thesis or Dissertation

Text version

ETD

URL

http://hdl.handle.net/10119/17470

RightsDescription

Supervisor:Dam Hieu Chi, 知識科学研究科, 博士

Japan Advanced Institute of Science and Technology

Customer Sentiment Knowledge Management inThai Life Insurance

Nanthawadee Sucharittham

Japan Advanced Institute of Science and Technology

Doctoral Dissertation
Customer Sentiment Knowledge Management inThai Life Insurance

Nanthawadee Sucharittham

Supervisor: Professor Hieu Chi Dam

School of Knowledge Science
Japan Advanced Institute of Science and Technology

March 2021

Abstract
This study explores service satisfaction with Thai life insurance based on
customer sentiments expressed on social media. This task provides an analyticalframework of customer sentiment knowledge management that shows how to benefit
from social media feedback through immediate problem-solving. After customer opinions
are identified through the sentiment extraction & analysis tool, the severity of problems isprioritized.
This research presents a new social CRM that manages knowledge using a
multidimensional sentiment cube to recommend processes that need to improve due to geta high volume of negative sentiment from user-generated content from social media. Thismethod is an emerging approach that takes benefit from the sentiment cube concept withdata mining. Text mining and natural language processing (NLP) is applied to extract
valuable knowledge chunks with their sentiments from critiques web-blogging in the Thai
language and then map each chunk to pre-defined dimensions in a cube.
In this work, a design of multidimensional sentiment cube on social CRM
was demonstrated in a case study of the Thai Life Insurance Industry; dimensions aredesigned with consideration of standard CRM factors as well as CRM components andaspect-based sentiment analysis. Besides, we present the results of an empirical study
conducted via a questionnaire survey. The concept of social customer relationshipmanagement (social CRM) is adopted by utilizing the sentiments expressed on social
media as part of an active management process to improve customer satisfaction, retain
customers, and recommend solutions positively received by respondents.
This study investigates service evaluation factors, claim settlement quality,
policy cancellation motives, and misunderstanding problems based on demographic
characteristics, life insurance attitudes, experience, and knowledge in life insurance ofThai respondents using the extracted topics from social media.

Keywords: Customer sentiment knowledge management, Multidimensional sentiment cube, Thai life
insurance, Social customer relationship management (Social CRM), Text MiningiAcknowledgements
I would like to express my sincere thanks to my thesis, extraordinary advisor,
Prof. Dr. Hieu Chi Dam1, for his invaluable help and constant encouragement throughout
this research. Prof. Dr. ThanarukTheeramunkong2.4,5 And to Prof. Dr. Ho Bao Tu6, Prof.
Dr. Mitsuru Ikeda1, Prof. Dr. Nam Van Huynh1, and Dr. Choochart Hareuchaiyasak3 fortheir useful suggestions and kindness in examining this thesis, including reviewers whorevised this work..
Besides, I am grateful for the Speech and Audio Technology Laboratory(SPT)3 members, which provide precious Thai language sources and valuable suggestions
to this thesis. Furthermore, I would like to express an additional thank you to Damlaboratory1 members, Dr. Nguyen Thu Huong1 and Takeyuki S. 1, KINDML members5,
JAIST friends & staff for their invaluable friendships and help to provide knowledge andprogramming recommendations.
Furthermore, I greatly appreciate the support to interviewees7-13 from manyorganizations for providing valuable system evaluations and precious recommendations tothis thesis. Especially, AIA life insurance section head, Payoongsak S. 7,10, Wipa C. 7,
Sivilai P. 7 and Vantanee S. 7 and AIA Thailand staffs7 who provided life insurance
knowledge to this thesis including section head of the other companies, Vichai J9, Naratip
J11, Jaturong K12, and Rattawadee A. 8 for providing service knowledge and questionnaire
survey.
Finally, I most gratefully acknowledge my parents and my friends for all theirsupport throughout the period of this research.

1. Japan Advance Institute Science and Technology (JAIST)

2. Sirindhorn International Institute of Technology (SIIT)

3. National Electronics and Computer Technology Center

4. Thammasat University (TU)

5. Knowledge, Information, and Data Management Laboratory

6. Vietnam National University

7. American International Assurance (AIA)

8. Thai Cardif Life Insurance

9. C.P. All Public company

10.Samart Telecommunication

11.Alliance for Supporting Industries Association (A.S.I.A.),12. GetSmart service

13. Total Access Communication (DTAC)

ii

Table of Contents
Chapter Title

Page

Abstract
Acknowledgements
Table of Contents
List of Tables
List of Figures

i
ii
iii
v
vi

1

Introduction

1

1.1 Significant and Problem
1.2 Research Problems
1.3 Purpose of Study
1.4 Research Questions
1.5 Significance of the study
1.6 Contribution of the study
1.7 Thesis Structure

1
3
4
4
4
4
4

2

6

Theory and Literature Review

2.1 Knowledge Management
2.2 Customer Knowledge Management
2.3 Life Insurance Definition
2.4 Traditional CRM and Social CRM
2.5 Life Insurance Knowledge Extraction with Data Mining Technique
2.6 Life Insurance and Sentiment Extraction
2.7 Sentiment Extraction with Unstructure data
2.8 Online Analytical Processing (OLAP) & Multidimensional Sentiment Cube
2.9 Customer Satisfaction in Life Insurance
3

6
8
8
10
13
20
25
29
31

Implementation

35

3.1 The Concept of Customer Sentiment Knowledge Management
3.2 Customer Sentiment Knowledge Management Process
Step 1: Identify and Access
1.1 Life Insurance Business Understanding with Social CRM
1.2 Sentiment Extraction Tool - "Samsarn Tagging Tool"
Step 2. Extract and Validation
2.1 Design of Multidimensional Sentiment Cube andits Dimension Structure
2.2 Dimension Structure Design on Aspect Identification from
2.3 Methodology of Multidimensional Sentiment Cube
Step 3. Utilize and Analysis Results
Step 4. Sharing
Step 5. Capture and Learning
Customer Sentiment Evaluation Questionnaire

35
37
38
38
44
48

iii

49
52
56
71
74
77
77

Step 6. Create and Leverage
Step 7. Implementation
Step 8. Learning and Monitoring
4

79
79
79

Experiment Results80

4.1 Sub Research Answer 1
80
4.2 Sub Research Answer 285
4.3 Sub Research Answer 387
4.4 Multidimensional Sentiment Cube Evaluation by Domain Expert &End-user 93
5Conclusion and Recommendation

105

5.1 Multidimensional Sentiment Cube Evaluation with previous works
105
5.2 Conclusion and Recommendation of Multidimensional Sentiment Cube107
5.3 Conclusion and Recommendation of Customer Sentiment Analytic Framework 107
References

109

Appendices116

Appendix A: Sentiment Cube Evaluation Questionnaire List
Appendix B: Survey on satisfaction of life insurance services in Thailand

117
136

This dissertation was prepared according to the curriculum for the Collaborative Education
Program organized by Japan Advanced Institute of Science and Technology and Sirindhorn
International Institute of Technology (SIIT), Thammasat University.

iv

List of Tables
TablesPage

2.1 Sample history demographic customer data

14

2.2 Sample product occurrences set

16

3.1 Life Insurance process related to CRM components and strategies

41

3.2 List of dimensions, sub-dimensions and measurements of multidimensional

55

sentiment cube
3.3 Clue terms with linguistic and non-linguistic words example

63

3.4 Sentiment scoring adjustment64

3.5 The analysis results in relation in topics, intentions and sentiments

67

3.6 Accuracy rate of each algorithm

68

3.7 An example of fact table aspectPattern_A71

3.8 An example of fact table aspectPattern_B72

3.9 An example of fact table aspectPattern_C

73

4.1 Respondent characteristics

78

4.2 Measures of customer dissatisfaction on service evaluation factors

88

4.3 ANOVA Results

89

4.4 Analysis of causes of policyholder dissatisfaction with claim settlement

91

4.5 Analysis of policyholder dissatisfaction with policy cancellations

91

4.6 Analysis of misunderstandings regarding life insurance principles
and regulations

92

4.7 Analysis of appropriate problem-solving methods for each service problem 92
4.8 An interview results 1

94

4.9 An interview results 2

944.10 An interview results of system design 1

95

4.11 An interview results of system design 2954.12 An interview results of system usage 1

96

4.13 An interview results of system usage 2

97

4.14 An interview results of system usage 3

98

4.15 An interview results of system validation 1

99

4.16 An interview results of system validation 2100

4.17 An interview results of system validation 3100

5.1 System comparison with previous works

106v

List of Figures
FiguresPage

2.1 The concept of knowledge management7

2.2 The different between TCRM and SCRM

12

2.3 The process of association rules mining

27

2.4 The methodology of customer sentiment knowledge management36

2.5 Customer sentiment knowledge management process38

3.1 Life insurance process across to CRM

40

3.2 Sansarn tagging tools on Intention screen Example 145

3.3 Sansarn tagging tools on Intention screen Example 245

3.4 Sansarn tagging tools on Engagement screen46

3.5 Sansarn tagging tools on Product screen47

3.6 Sansarn tagging tools on Process screen 1

47

3.7 Sansarn tagging tools on Process screen 2

48

3.8 An example of a multidimensional sentiment cube150

3.9 An example of a multidimensional sentiment cube251

3.10 Customer Acquisition Stage in Life Insurance with line A and B

53

3.11 Customer Engagement & Retention Stage in Life Insurance

53

3.12 Multidimensional Sentiment Cube Methodology

57

3.13 Service Touchpoint Hierarchy57

3.14 Service Process: Policy operation & Claim assessment Hierarchy

58

3.15 Product (Plan) Hierarchy

58

3.16 Impact Type Hierarchy

58

3.17 Related Aspects Hierarchy

58

3.18 The example of collection of clue terms for intention and topic analysis

66

3.19 Process of aspect/sub-aspects mapping using lexitron dictionary& clue terms

69

3.20 Customer Sentiment Analytic Framework75

4.1 An analysis example of customer acquisition 1

82

4.2 An analysis example of customer acquisition 282

4.3 An analysis example of customer retention 1

83

4.4 An analysis example of customer retention 2

84

4.5 An analysis example of customer termination 1

84

4.6 An analysis example of customer termination 2

85

4.7 The industry positions who proper to use this system95vi

4.8 The evaluation of system design

96

4.9 The evaluation of system usage97

4.10 The evaluation of system validation

98

vii

Chapter 1
Introduction
1.1 Significant and Problem
According to the Swiss Re Institute's sigma research, the global insurance
market will experience aggregate emerging-market growth of around 4.9 percent
annually between 2019 and 2020, following 4.7 percent growth this year (2018).
Thailand is an emerging market with continuing growth in the life insurance market. In
2016, Thai life insurance ranked ninth among the top 15 emerging marketing of the 40
largest markets by life premium volume.
According to the Thai Life Assurance Association (TLAA), Thailand's life
insurance status at the end of 2017 was as follows. The number of inforce policies was
7,939,652. The number of new active policies was 1,292,748 (up 16.28%); however,there was a loss in volume of current inforce policies of 272,117 surrendered policies
and 485,393 lapsed policies (a drop of 9.54%). Based on this, we found several
encouraging signs for the life insurance industry in Thailand. However, the insurance
market also faces the obstacle of policy loss through cancellations and lapses. The highvolume of policy loss shows the significance that there are hidden problems that require
attention. One way to reduce policy losses is to increase customers' satisfaction with
their policies.
Customer satisfaction is an indicator of an organization’s success, mainly when
it includes the service quality dimension. Therefore, an organization can use customer
satisfaction to manage and develop its business [71], increasing the persistence rate forlife insurance. Nonetheless, it is not easy to increase customer satisfaction in thisindustry. Life insurance is an intangible service, and its lack of physical dimensions
makes it difficult to measure service satisfaction. The service characteristics areintangible and not readily defined. Moreover, the service delivery process is targeted atcustomer satisfaction and not at a physical good. Furthermore, the service isheterogeneous, as it has no exact service standard. It also depends on the quality of each
personal contact [21]. These reasons have motivated many researchers to study
customer satisfaction in an intangible service industry, such as life insurance.
Previous studies on customer satisfaction in the life insurance industry haveoften focused on identifying important indicators that impact customer satisfaction in a
specific area. For example, Subashini and Velmurugan (2016) studied policyholder
satisfaction in Coimbatore District, India [78]. Kannan (2018) also studied customer
satisfaction in Chennai, India [40]. Some scholars have had a specific purpose or
objective, such as Kuhlemeyer and Allen (1999), who explored consumer satisfaction
in life insurance products purchasing (Kuhlemeyer & Allen, January 1999). Nguyen etal. (2018) presented the determinants for customer satisfaction and loyalty in life
insurance services in Vietnam, and so on [43], [59]. Previous studies have focused on
the passive management of problems, using questionnaires that include questions
framed by service-provider experts constituting knowledge from the inside-out. For theactive management of problems, the questions need to be based on outside-inknowledge by identifying problems from customer criticisms expressed externally, forexample, on the internet. Resolving these problems before they become severe isessential. Therefore this research examines the severity of service dissatisfaction basedon customer criticisms expressed on the internet. It uses the concept of social customer
1

relationship management (Social CRM) to identify high-severity problems. In general,the goal is to increase customer satisfaction by resolving the main problems that cause
the most dissatisfaction.
CRM is traditional strategic method companies use to manage their relationshipwith customers [79]. However, the emergence of social media has challenged CRM'straditional concept (Weinberg et al., 2011). Traditional CRM assumes that customers
are passive, responding to a company's action primarily through purchasing behavior(Malthouse et al., 2013). The new concept of social CRM considers customers as active
participants [46], [70], [74]. Social CRM (SCRM) means building relationships withcustomers based on social interaction. It extends the traditional customer relationshipmanagement concept by helping companies communicate better and create more
effective relationships with customers, thereby enabling them to respond online. In thiscontext, communication and information sharing occur quickly, and companies respondto customers' needs more accurately. For example, when a customer generates content
on social media related to a specific brand, they are engaging with that company as anactive customer.
In the context of Thai life insurance, sharing experiences on a product or service
from social media often be blown out of proportion. When customers experience poor
service, they usually prefer to share this experience with their friends and social media
rather than to submit complaints directly to the service provider [46]. These negative
messages affect the attitudes and opinions of other customers. Thai review blogs, suchas Pantip.com, Krapook.com, which are free sites, contain numerous posts on service
dissatisfaction, leading to negative word of mouth.
Knowledge management on customer sentiment, such as utilizing thesentiments expressed on social media to improve customer satisfaction as an active
management concept of social customer relationship management (social CRM) is
essential. The customer sentiment extracted from social media can be an initial source
for sharing an understanding of customer behavior and finding out the improvement
methods in order to increase customer satisfaction as co-creation new perspective ofproblem-solving. Especially, negative sentiment can utilize for finding problem-solving
methods by sharing those sentiments to survey their opinions from various
demographics customers based on the problems related to that negative sentiment using
a questionnaire.
However, sharing an extracted customer sentiment from social media between
the customer who cannot identify the characteristic of customers (as anonymous
source) and customers who can identify qualifications and demographics in order to get
recommendations about that sentiment is not easy. To propose the methodology toutilize the knowledge from customer sentiment for improving customer satisfaction in
Thai life insurance, knowledge sharing process is a key important to merge knowledge
between outside-in and inside-out can reveal customers’ mind. This process requires
the critical key elements. It consists of business knowledge understanding, analysis
concept understanding, and sentiment extraction technology, and sentiment analysis
concept. This study chooses the Thai life insurance industry to be a case study, one of
the extensive services and impacts on Thai society.
To identify service-dissatisfaction issues related to insurance from such webblogs requires knowledge on extracting customer opinions requires technology such as
natural language processing (NLP), text mining, or sentiment analysis (Liu, May 2012),(Fangtao et al., 2010), (Minqing & Bing, 2004) [10, 31, 32, 45]. It can help develop atool that extracts customer sentiments from the text by identifying the negative words
2

related to each aspects of Thai life insurance service. This research developed asentiment extraction tool and analysis tool named multidimensional sentiment cube [60,61]. The multidimensional sentiment cube results show that the customer dissatisfaction
scale prioritizes the crucial problems with sentiment scoring. The causes ofdissatisfaction in the claim settlement process, policy cancellation motives, andmisunderstanding between policyholders and service providers are provided.
In summary, this research has purpose to present the methodology for utilizing
customer sentiment to improve customer satisfaction in life insurance service using
technology and knowledge sharing. Out analysis tools “Multidimensional sentiment
analysis” can analyze the existing problems from negative customers’ sentimentextracted of web-sites, next synthesis analysis results to be knowledge from customer
sentiment. Then, make knowledge sharing process by conducting the questionnaire tofind out appropriate problem-solving methods to deal with dissatisfaction, The results
are from respondents who have different qualifications based on demographics such as
gender, age, income, and education and hold different life insurance, experience,
knowledge, and attitudes.
Finally, this research reveal the suggestions to life
insurance service provider by providing practical recommendation on current problems
and create new knowledge to get better customer satisfaction.
1.2 Research Problems
1. Life insurance is the sound-reducing the effect of unexpected vital accidents by
sharing risk among a group of insurance members. Most people usually do notrealize its importance, its benefit. Moreover, life insurance knowledge iscomplicated and challenging to understand the rules and regulations clearly.
With this, a reason makes Thai life insurance does not grow so fast. Also, life
insurance is one intangible product. It is challenging to realize the use of that, so
it is essential to clearly understand the customer in dissatisfaction problems bymaking use of customer sentiment. Especially, service industry such as life
insurance has intangible characteristics because of it is difficult to identify
aspect such as product. For example, camera has main aspect on lens, weight,
price, etc. While to identify service aspect require knowledge such as businessprocess knowledge or service knowledge.
2. While turning to the Internet society, many industrials require adaptive
management. To take into account under social CRM is the one useful concept
for adaptation of business. Utilizing user-generated content for sharing
knowledge with customers can present customer satisfaction levels and deliver a
new perspective using customer sentiment knowledge management. Sharing
knowledge inside-out information (passive) and outside-in information (active)
require customer sentiment knowledge management.
3. In social media era, increasing efficiency of information analysis requires
technical knowledge related to sentiment analysis. Making an application
becomes more practical for real usage in sentiment extraction from social media
is necessary by concerning the relationship among multiple business factors
such as a hierarchical view based on negative, positive, or neutral. Goodvisualization, which presents the analysis viewpoints among multi-factors is
required. Since the relationship is across different features, it reveals moremeaningful analysis and can reveal hidden problems by getting the3relationship's negative opinion. For example, the A. feature represents a hidden
problem when getting a negative sentiment score from A1, which is under A. If
A1 has high-frequency co-occurrence in a negative score, it shows A1 canpresent A. is a significant problem as well.
1.3 Purpose of Study
To propose the methodology to utilize customer sentiment knowledge for
improving customer satisfaction in Thai life insurance
1.4 Research Questions
Main Research Question: How to utilize the knowledge on customer sentiment for
improving customer satisfaction in Thai life insurance?
Sub Research Question 1: How to develop analysis tool for extracting customer
sentiment knowledge from social media?
Sub Research Question 2: What are the issues relate to customer sentiment in Thai
life insurance from knowledge sharing process?
Sub Research Question 3: What kinds of knowledge on customer sentiment havebeen utilize for knowledge sharing process between service provider and customer?
1.5 Significant of the study
Academic Significance:
To present the methodology to utilize customer sentiment in order to improve customer
satisfaction in Thai life insurance service using technology and knowledge sharing.
Social Significance:
To reveal a suggestion to life insurance service providers by providing practical
recommendations on current problems to get better customer satisfaction
1.6 Contributions of the study
To propose the methodology for utilizing customer sentiment from social media toimprove customer satisfaction in Thai life insurance service by knowledge sharing
between outside-in and inside-out knowledge
To propose the methodology in an analysis tools in sentiment analysis called
multidimensional sentiment cube by deploying the benefit from OLAP. It make thistools efficiency to analysis unlimited viewpoints which more high efficiency for
sentiment analysis than other tools such as single or hierarchical aspect which limitedview.
1.7 Thesis Structure
The rest of this thesis structured is as following:
1.7.1 Chapter 2This chapter is a literature review of a related research topic and previous
researchers' previous work. The details are previous and current works from many
researchers related to our research in this thesis. The corresponding theory consists of4

knowledge management, customer knowledge management, Thai life insurance,
traditional customer relationship management (CRM) & social customer relationshipmanagement (social CRM), life insurance knowledge extraction with data mining,sentiment extraction for life insurance, technology related to sentiment extraction in
Thai language (text mining, natural language processing (NLP)), a hierarchical concept
in online analytical processing (OLAP), and multidimensional cube.
1.7.2 Chapter 3This chapter shows the background and idea of implementing customer
sentiment knowledge management and its process in Thai life insurance. The concept is
explained as a knowledge management process, including the implementation of
support tools; the multidimensional sentiment cube design is described and illustrates
detailed implementation, including information extraction and sentiment calculation.
Also, the sentiment knowledge sharing using a questionnaire is explained.
1.7.3 Chapter 4

Their research results are explained in this chapter, including examples ofmultidimensional sentiment cube analysis scenarios and domain experts and end-users'
evaluation. In addition, the results of customer sentiment knowledge sharing using
questionnaires are included.
1.7.4 Chapter 5

Finally, discussion and conclusions are made in this chapter. It consists ofmultidimensional sentiment cube comparison to previous works, conclusion, andrecommendation from multidimensional sentiment cube analysis results. Also, ananalytical framework of customer sentiment is in this chapter.

5

Chapter 2Theory and Literature Review
This chapter discusses the relevant theories and investigates the previous andcurrent researches related to this research thesis. Literature review gives explanations
about the corresponding research about the knowledge management, customer
knowledge management, Thai life insurance, traditional customer relationshipmanagement (CRM) & social customer relationship management (social CRM), life
insurance knowledge extraction with data mining, sentiment extraction for life
insurance, technology related to sentiment extraction in Thai language (text mining,
natural language processing (NLP)), a hierarchical concept in online analyticalprocessing (OLAP), and multidimensional cube. The previous research about customer
satisfaction in life insurance service is also reviewed. Many previous works aredescribed and analyzed to get more information and understanding for applying to our
method.
2.1 Knowledge Management
Knowledge Management, also known as KM, is a management approach for
operating within the organization to make definitions on corporate knowledge,
including collecting, creating, and distributing knowledge of the organization
throughout the organization to build up the knowledge and apply knowledge to gain
benefits. It also creates a culture of learning within the organization, namely thecollection of existing knowledge scattered in the person or document. To develop asystem, everyone in the organization can access knowledge and improve themselves as
an educator. Moreover, apply the knowledge to make the operation more effective.
Knowledge comes from education, research, or experience, including practical ability
and understanding skills or information obtained from experience received fromhearing, listening to thoughts, or practices in each area. (Source: Royal Institute ofThailand (RIT).
Knowledge is an integration of experience, values, contextual information, and expert
insights with a framework for evaluating and integrating new experiences and data. It
arises and is applied in the minds of knowledgeable people. It is frequently embedded
in documents or repositories and organizational routines, processes, practices, andnorms in an organization. (Working Knowledge: How Organizations Manage What
They Know).
Knowledge Type:
Explicit knowledge is knowledge derived from theories, definitions, or manuals thatanyone can access or learn. It can be transmitted through various methods such as
written notes, manual theories, documents, rules, and procedures for media operations
such as VCD DVD Internet. It also can be called “Concrete knowledge.”
Tacit knowledge is the knowledge inside of individual persons, such as experience,
talent, instinct, or flair of each person to understand the work or information. This6

knowledge cannot easily be conveyed through letters. Therefore it sometimes can becalled “Abstract knowledge,” such as work skills, crafts, experiences, ideas.
Elements of Knowledge Management:
Knowledge management relies on three main components as follows:- People, people or personnel are the most important factors in knowledge
management implementation because people are the most important source ofknowledge and people who will bring knowledge through organizational
development.
- Technology is a search-exchange assistant. The key is to store data for enabling
people to utilize various kind of knowledge into organizational development.
- Knowledge process Is management in every Steps of knowledge management
In every part Must be related in a balanced way Including efficiency
The concept of knowledge management:

Figure 1: The concept of knowledge management
1. Identify and Access knowledge: to consider what the organization has a vision,
mission, strategy, and goal to achieve the goal. What needs to be used? What
and where is the current knowledge? And from whom?
2. Extract / Validation: understanding, synthesis and validation.
3. Utilize / Analysis Results: for improving, modifying, or creating someknowledge to be suitable for organization usage.
4. Utilize / Analysis Results: for improving, modifying, or creating someknowledge to be suitable for organization usage.
5. Capture and Learning: make use of knowledge to support making decisions,problem-solving, and make a part of the work, such as learning systems fromknowledge creation.
6. Capture and Learning: make use of knowledge to support making decisions,problem-solving, and make a part of the work, such as learning systems fromknowledge creation.
7. Implementation and Learning: to make continue to circulate continuously.
7

2.2 Customer Knowledge Management (CKM)
Customer Knowledge is one of a strategic resource in a company success. CKM
is a necessary domain of Knowledge Management (KM) and it combines theconnection between KM and Customer relationship management (CRM). CKM is an
management principle which KM is an instrument to support the knowledge
management between customer and organizations. It uses for manage customer
relationship in order to improve CRM process such as customer acquisition, customer
retention etc.
CKM focuses on defining customer knowledge, sources and types. There aresome CRM previous works. For example, Gebert suggested the CKM model on fourknowledge aspects: 1) content, 2) competence, 3) collaboration, and 4) composition.
Rollins et al. stated CRM is a learning process where organizations make process ofknowledge exchange in efficient way. They has purpose to make understanding on an
integrated management approach of CKM.
Previous works example of customer knowledge management, Rollins &Halinen (2005) gave an understanding of the customer knowledge management that it
requires the way to integrate a tentative theoretical framework as an integrated
management approach. Jay (1990) explained the six necessary ingredients forknowledge management which will increase a chance to success of company. Ranjit &Vijayan (2003) suggested the CRM framework through the knowledge management
technology which can be basis of CRM improvement. Wu, Guo and Shi (2013)provided a conceptual framework to explore the linking mechanisms between IT-basedon business and customer knowledge management for model innovation including
integrated both customer perspective and firm perspective for helping company
understand the linking mechanisms and gain more benefits.
2.3 Life Insurance definition
The definition of life insurance in this study
Life insurance is a contract between an insurer and a policyholder in which the insurer
guarantees payment of a death benefit to named beneficiaries upon the insured's death.
The insurance company promises a death benefit in consideration of the payment ofpremium by the insured. The purpose of life insurance is to provide financial protection
to surviving dependents after the death of an insured.
Life Insurance Basic is the primary insurance that will be the insurance that protects
the insurer in two ways: when living or die. If the insured lives up to the last day of thecontract, the insured will receive insurance plus additional benefits (if any), but if theinsured dies before the last day of the contract, the company will pay (the sum insured)to the beneficiary, which the insured can specify to give to anyone.
Life Insurance Riders
Many insurance companies offer policyholders the option to customize their policies toaccommodate their personal needs. Riders are the most common way a policyholder
may modify their plan. There are many riders, but availability depends on the provider.
Life Insurance Company / Service provider is a company licensed by the Minister ofFinance to operate a life insurance business to guarantee loss or damage to individuals
8

or groups of people by promising to pay compensation to the insured or beneficiary incase the insured has died including other coverages as contract.
Life Insurance Agent is a person who is assigned to persuade the person to make aninsurance contract. An insurance agent may be an employee of an insurer or a third
party.
Insured is a person who is protected by insurance policies in life insurance.
Premium payments are set using actuarially based statistics. The insurer will
determine the cost of insurance (COI), or the amount required to cover mortality costs,
administrative fees, and other policy maintenance fees. Other factors that influence thepremium are the insured’s age, medical history, occupational hazards, and personal risk
propensity. The insurer will remain obligated to pay the death benefit if premiums are
submitted as required. With term policies, the premium amount includes the cost of
insurance (COI). For permanent or universal policies, the premium amount consists of
the COI and a cash value amount.
The death benefit is the amount of money the insurance company guarantees to thebeneficiaries identified in the policy upon the insured's death. The insured will choose
their desired death benefit amount based on the estimated future needs of surviving
heirs. The insurance company will determine whether there is an insurable interest andif the insured qualifies for the coverage based on the company's underwriting
requirements.
Hospital and Medical Expenses insurance is one type of insurance that protects
medical expenses.
Policy Lapsed is an expired or terminated policy when the insured does not pay thepremium at the time specified in the policy contract.
Policy Maturity is the end of the life insurance contract, which the insurer had paid
total premium according to the insurance contract.
Premium is the amount paid to the life insurance company to purchase the coverage by
paying in installments until the end of the contract (As agreed in the payment terms)
Sum Assured is the amount specified in the policy, defined as benefits, which must be
paid when an insured event occurs.
2.4 Traditional CRM and Social CRM
Most companies have many tangible assets to support their businesses, such as
knowledge workers and business premises. However, the greatest asset that a company
has is the customer, as expressed by Compbell and Cunnigham (1983) [41].
As the most significant asset for a company, customer satisfaction is the main
target for every company to achieve its business goal. To increase customer
satisfaction, which is the key to success in business, customer relationship management

9(CRM) is applied as a strategic tool for a company to build, manage and strengthen therelationship with its customers [15] [47] [58].
Customer Relationship Management (CRM) definition isa.
Develop and maintain a mutually beneficial long-term relationship between theorganization and its target customers with business strategy by concerned customercentric.
b.
It is an organization's strategy that tries to reach customer feelings, understandcustomer behavior, and persuade a customer by creating customer relationships forcustomer acquisition, customer retention, and royalty, including increasing customer
profitability.
Customer Relationship Management (CRM) forms is including.
1. CRM Functional and operational means process and technology which deploy
to provide the best service to an organization's customer for increasing
efficiency and accuracy in the operation process of organization
2. CRM customer segmentation uses customer lifetime value information andprovides service in a specific group of a customer by understanding customer
behavior in time. CRM can provide a fashionable product, improve service andproduct by innovation as customers' needs have changed fast.
As the era of social media, there are increasing the challenging of the traditional
CRM framework to manage the substantial information of the customer to manage
relationship among them. (Payne and Frow 2005; Verhoef, Venkatesan, et al.
2010)[47]. A typical CRM usually performs a statistical analysis of sales/service
records and customer feedback to reveal customer participation patterns or behavior.
With the fast growth of ICT, both sales/service records and customer feedback aredigitalized and even acquired online via a web browser or social media. The client is nolonger limited a passive role in the relationship with the company. [47]. Moreover, thecompetitive of product will increase in various media such as mobile devices. It is trend
to increasingly difficult to manage the product/service information to customer in orderto gain better percept. (e.g., Schultz, Malthouse, and Pick 2012) [47].
Harvard Business School's view on customer loyalty research about "Customer
referrals, endorsements and spreading the word" has significance for customer
behavior, impacting an organization, products, or service by "Word of mouth,"
especially in the customer acquisition stage. For example, "Would you like torecommend other people to buy or request service from our company?" Thecompany can get the correct answer from a customer more than ask directly tothe customer that it will return to use our service or not. These are significant
issues that are ignored by many organizations now.
For this reason, many researchers have begun to study the social CRM which
emerges along the border between CRM and social media. Researchers are engaged insocial media impact on traditional CRM problems such as customer churn (Nitzan andLibai (2011) and Haenleim (2013) [47]. Much research is related to the consequence ofword-of-mouth acquires customers compare to other channels (Trusov, Bucklin, andPauwels 2009; Villanueva, Yoo, and Hanssens 2008). Furthermore, the result of suchresearch found that the viewpoint of customer value has changed. The customer value10

is no longer limited to purchase-based and includes social elements such as customer
influence, referrals, and knowledge (Kumar et al.2010; Weinberg a dBerger 2011).
Thus, there is some research about the design of the social CRM framework,
which examines how CRM needs to adapt to the rise of social media to understand howsocial media affect CRM. They identified the company's customer engagement level onsocial media, which is affected by its approach to three components of traditional CRM.
It consists of Acquisition, Retention, and Termination [47].
Customer acquisition: Companies seek to enlist social media in their customer
acquisition efforts begin by uploading advertising spots on social media such as
YouTube, Facebook, etc. This action can help companies create awareness and change
attitudes among prospective customers, thereby contributing to new customers'
acquisition [47].
Customer retention: Company can incorporate social media to retain existingcustomers and maintain an ongoing relationship with customers using social media topromote new campaigns for current customers [47]. Social media's viral benefits forcustomer retention are finding out the problems that scatter on critiques on various
media. Usually, people will not return to the back company to give feedback directly ifthey do not face any severe problems and request help from the company. At this point,
it makes the company miss important dissatisfaction information from a customer. It
may have significant impacts on business growth in the future. However, people arefamiliar with expressing feeling both negative and positive on social media. It is one of
the company's opportunities to do an active process like promotions on social media
and do a passive process by finding out information such as the dissatisfaction feelingof a customer on social media.
Customer termination: A customer termination case is possible to initiate
either by the customer or by the company. Traditional CRM can use historical recordsof customer activities/behavior to find out a pattern for predicting churn. All of theterminated customers' records come from the database, which key-in/input from thesurvey process or complain system. Both of process is a passive process afterterminated problems have occurred already. The answer to each problem/or
dissatisfaction problem was led by a human/expert who only makes a questionnaire.
Currently, social CRM can find out more benefits from finding a real cause oftermination. The company can do active action by finding out the dissatisfaction
problems that terminated customer posted on social media freely and can find more
hidden problems out of the frame of expertise in this area. However, the limitation offinding information from social media is that we cannot find the real cause of negative
sentiment in every terminated case. It depends on terminated customers express feelingon social media or not, and our system can track these problems.
Social media influences customer engagement in company approaches
composed of customer acquisition, customer intention, and customer termination in thesocial CRM conceptualization. This concept is derived by analyzing the large quantities
of data from customers' critiques records posted on social media. Information
technology can search for knowledge by using various data platforms, including
unstructured data from social media. After knowledge is extracted, the next step of the
CRM process/step is to set up a strong strategy by management level and strategic
expertise. Strategic preparation is the step of making the market of this strategic andfollow-up and feedback process also need to set up in advance for the outstanding
11

performance of results. In a part of strategic information, social CRM is highly
concerned about the sentiment on social media. Many customers feel free to express an
opinion on various websites that cannot feedback to the company directly are scattered.
Businesses can gain benefits from this opportunity from customers' sentiment analysis.
In the concept of social CRM, it is essential to study CRM's current practice inthe business field. How technology, especially information technology, can increase theability of CRM to a new generation of social CRM based on customers' opinionatedcomments from a particular website (41). However, today's CRM system cannot benefit
from this massive amount of information because it focuses on the purchase pattern inhistorical data of the CRM database, which keeps data records from daily company
records. To enhance the CRM system, emerging the components of technology andsentiment analysis, people's opinion is necessary. This task used social CRM under
traditional concepts to declare features (aspects) for sentiment analysis using techniquessuch as data mining, text mining, and multidimensional database for presenting analysis
viewpoints of the orientation of customer's opinions regarding a particular product orservice.

Figure 2: The different between TCRM and SCRM
There are differences between the Traditional CRM process (TCRM) and theSocial CRM process (SCRM). TCRM focuses at the end of the value chain, such as theforecast process, using historical data such as customer profile and customer activities.
For example, to utilize data from the current marketing campaign to generate lead of
campaign then convert into opportunities and feed into forecast process to predict.
Besides, TCRM is one-way communication as only an inside-out management
approach. SCRM has concerned with a meaningful relationship. In the social media era,
the various contents on websites provide customer sentiment on conversations. Thosekinds of content can spread widely on social media and impact customer opinion when
making decisions on products or services. SCRM is two-way communication between acustomer and business owner as of the inside-out and outside-in management approach.

122.5 Life Insurance Knowledge Extraction with Data Mining techniqueLife insurance business requires information for making a decision on many
functions, and currently, they deploy various technologies to support information
extraction and analysis such as a statistic, data mining, text mining, etc. This section
describes the examples of data mining techniques applied to extract knowledge fromlife insurance. The survey covers data sources, mined knowledge, and mining
techniques.
As a classic service but complex structures, insurance becomes an interestingtarget field of study for service science. Currently, due to various conditions and needs,a large number of insurance types and policies are developed. With a suitable type andpolicy, the customer and/or his/her family can sufficiently obtain income when an
unexpected inconvenient event, such as an accident, sickness, or fatal death, takes
place. Looking as a whole, good insurance plans can reduce the risk of both customers
and insurance companies. However, life insurance is also not so popular in mostdeveloping countries, including Thailand, due to the difficulty of insurance
terminologies, the complexity of insurance policies, and the intricacy of benefit
assessment. Consulting proficiency is required to extract a customer's behavior and lifestyle from massive data to describe each policy's effect on the customer and determine
the level of coverage for individual people [1]. Recently information technology has
been developed to the level of practice.
Knowledge utilization provides an insightful understanding for people andorganizations, such as allowing an operation by their demands, making coordination
between the parties, or facilitating information management to make better decisions.
Knowledge extraction is the process of exploring and constructs knowledge fromvarious sources of data. This knowledge is extracted into solid results for easy
understanding, and we can utilize this useful knowledge to support our purposes. Oneof the valuable data sources in a company is its database storing the customer activity
records. It is possible to analyze customer behavior from such a database, including
both structured and unstructured data. For instance, the structured data might consist ofdemographic information, including private information. An unstructured data isnarrative information such as opinions from customers and evaluation results ofunderwriting. By utilizing knowledge extraction from these structured and unstructured
data in the insurance industry to analyze customer behavior, we can understand how to
obtain and dispose of products. A new customer can consume this knowledge acquired
from the current customer behavior if there are the same behavior, lifestyle, andconditions. However, it is not straightforward to figure out which insurance policy canbe recommended to a specific customer. Even if the most similar customers, they may
occupy different minor characteristics, lifestyles, and conditions. Customer behavior
can be exploited by using knowledge extraction to support various purposes frominsurance activities. The initial criteria which can determine the basic rules for
assigning an insurance type to an individual customer have three viewpoints.
2.5.1 Customer segmentation extraction
The importance of customer segmentation is to make new customers know theircurrent positions, which segment matches up character or lifestyle. Because thecustomer separations are used for separating the group by determining the similarity ofmembers, they make more understand each customer segment's unique characteristics.
13

Generally, the insurance company gets demographic data of customers when acustomer made a new contract. They can utilize customers' demographic data such as
education, gender, income level, marital status, age, premium, and life stage forextracting customer segmentation. A data mining technique is called It is a "clustering
technique" which tries to group a given data into a class so that the members within thecluster are highly similar in comparison to one another. But these members are very
different from other clusters. Similarities or differences are evaluated based on attributevalues describing the element using distance measurement. Generally, there are manyof clustering techniques such as hierarchy method, partitioning method, K-meanclustering method, density-based method. However, we give an example of how theclustering technique works in customer segmentation from K-means clustering.
Customer segmentation can perform extraction using clustering techniques
based on determined attribute values to create new catalogs. The expected result fromthis analysis target is to find out the position of newcomers in the current insurance
market. A new customer will know which segmentation matches his character or his
lifestyle by demographic customer data analysis.
Refer to Table 1; there are provided attributes from history demographic
customer data such as gender, age, occupation, income. An analysis attribute is the life
stage attribute. Customers who pay attention to making an insurance contract should
know their position from the results after exercise clustering techniques.
Gender
Male
Male
Male
FemaleFemaleFemale

Age
7
21
61
14
35
55

Occupation
Student
Soldier
No jobStudent
Officer
Teacher

IncomeLife Stage0
Juveniles
15,000 First Jobbers
15,000Retirees0Juveniles
35,000Matured
65,000MaturedTable 1: Sample history demographic customer data
The data from the determined attributes can perform clustering to separate thecluster of the customer. In brief, the steps of K-means consist of do partition objects
into non-empty subsets, compute the mean point of the clusters of the current partition,
assign each cluster center to the mean of its assigned item and then repeat untilconvergence. Since clusters are not predefined, a domain expert is often required tointerpret the created clusters' meaning. After taking an experiment from K-meansclustering, the supposed results of a first cluster are male, 32, soldier, 16,500, firstjobbers, and a second cluster is male, 35, officer, 25,000, matures in terms of gender,
age, occupation, income, and life stage. Next, we put test data as male, age is 30,
occupation is officer, and income is 20,000. In this case, the cluster of this test data iscluster one, and the life stage should be first jobbers. It might be different from findingout a result from fundamental analysis, which may classify this case to be matured.
Many researchers often utilized clustering techniques in their tasks. Various
papers used K-means algorithms in their experiments, such as A.B. Devale and Dr.
R.V.Kulkarni used the K-means clustering technique to classify a customer's groupfrom demographic information such as gender, age, income, education level andoccupation [72]. This section introduced the basic concept of each data mining concept
for knowledge discovery in the insurance business. In clustering, they used K-Means togive an example if a company does not have any predefined labels. They presented
14

based on the grouping outcome; they will target marketing and advertising campaigns
to the different groups for particular types of policy. Shu-Hsien Liao et al. utilized theconcept of data mining in real work to determine the level of customers' requirements
in life insurance products by generating the knowledge rules and patterns from theirdemand chains of customers [98]. This paper also used K-means clustering to classify
the customer group from demographic data, which is quite different. They usednecessary information such as name, gender, birth date, and life stage needs, residential
house level, life partner, and transportation mode. Depth details of features candistinguish the classification of customer groups more clearly.
Many previous works classified customer groups for different purposes from theclustering method. Not only the clustering method but also other methods can utilize
for customer segmentation. For instance, the association rules are widely utilized,
especially market baskets theory for finding existing customer patterns such as Yu Yan
and Haiying Xie recommended using association analysis in the customer behavior
model to improve the value of a customer in the customer relationship managementtask (CRM) of insurance industry [97]. For example, the recommendations of new
insurance service to an old customer to upgrade sales volume and improve the return
rate of sale-investment. Moreover, they also gave some recommendations about a basic
level of informatization in pattern construction. Yongqiang Chen & Leifang Hu
employed data mining knowledge into customer relationship management (CRM) in
the insurance business to enhance the customer value model and market classification
model [49].
2.5.2 Occurrence product patterns extraction
Finding out a suitable life insurance product for each individual is difficult
because various kinds of insurance products are basic product types and rider
concurrence products. They are challenging to understand the usefulness of eachproduct's different purposes and difficult to know which group of a product should beheld simultaneously to support the highest benefits.
- At present, selling channels or agents still offer a new product to a customer
from their experience without considering the highest benefit of a customer.
Because it is not straightforward to understand clearly the relationship of
product benefits between basic and rider, so they cannot recommend the rightproduct to the right person correctly. Usually, they offer base on their idea andsometimes base on their commission of each product at that time. Usually, life
insurance product has two product types. The first type is called a basic product,
which means one main policy has only a basic product for each insured. The
second type is called a rider product, which is a supplement product into a basic
product. Each basic product has a different purpose. Some of the basic producthas a purpose of building up cash value, and others are a protection purpose in
the short term. The example of a basic product in different purposes such as [67]Term life insurance is for a limited time, such as 10, 15, or 20 years.
Universal life insurance is flexible and allows increasing premium over time
Whole life insurance is permanent insurance and provides lifetime coverage.
The premiums are fixed and build cash value - the value functions as a saving accountthat may be tax-deferred.
Rider product is the additional benefits that can be added into a basic product. Rider
product has increased a level of coverage and can be blended, for an additional cost,15

according to future coverage needs [38]. Here are some examples of standard riders inlife insurance.
- Waiver of Premium (WP) is a rider attached to the basic policy to maintaincoverage in the event of total and permanent disability; subsequent premiums
are waived.
- Hospital & Surgical Benefit (H&S) rider provides reimbursement for medical
expenses in case of hospitalization in a licensed hospital as an in-patient (IPD)- Accident Death Benefit (ADB) is a one-time payment after the insured isdiagnosed with a terminal illness that will considerably shorten their lifespan.
Each insured hold the different kind of riders within one policy which are under
only one basic product. That means it has a different kind of riders that are often soldtogether. It is necessary to search the product group's pattern that suits each customer tosolve this problem. Data mining techniques, such as association rules, can help extract
the hidden product concurrence group from previous history purchasing of customer
data. The target results are finding all the association patterns from various historicalcustomer data features that present the customer's behavior when they bought products
as a subset of frequent item sets. Most of the time, they also bought the remaining itemsin the same frequent item set.
Many researchers used association rules mining techniques such as the Apriorialgorithm and market basket analysis, which are techniques for generating rules from alarge data set based on the co-occurrences among the items. This analysis aims to learn
life insurance item sets of basic and rider product concurrence to understand whatcustomer is demanding, and these patterns can be used for recommending product sets.
The concept of association rules is below.
Association rules mining results in the implication of the form X => Y (If X
then Y), where X and Y are the item sets
Step 1: finding the candidate item sets which pass the minimum support1; Support (X=> Y) = P(X and Y)
Step 2: generating rules from item sets which pass the minimum confidence2 (accuracy)
Confidence (X=>Y) = P(Y|X) = P(X and Y)/P(X) = support (X => Y)/support(X)Item ID
T1
T2
T3
T4
T5

Basic
Term
UL
WL
UL
WL

Item sets
Rider
WP
H&S1
WP
ADB
WP
H&S2
WP
H&S1
WP
H&S1

H&S2

ADB
H&S2

Table 2: Sample product occurrences set
Remark: UL – universal life, WL – whole life, WP = waiver of premium, H&S1/2 = hospital & surgical benefit type 1/2,
ADB = accident death benefit
1
2

Support factor is an indicator that informs how much occurrence of the events X and Y.
Confidence factor is an indicator that informs how many percentage of Y happen when X happen.

Table 2 is a sample product occurrence data set consisting of basic and rider
products in these insurance policies. We present this example from the association rule
generation with 3-itemsets rules under condition minimum support = 30% andminimum confidence = 80%. The results found that when people bought one basic
16

product, 100% bought one waiver of premium (WP) and at least one type of hospital &surgical benefit (H&S). In terms of rider occurrences, 100% when bought accident
death benefits (ADB), people bought waiver of premium (WP) and hospital & surgicalbenefit (H&S).
Association rules mining plays an important role in finding out the hidden
pattern from basic product sell concurrence with rider product. Many researchers also
use a statistical concept to analyze this point, such as K-nearest neighbor, Pearson
correlation. A.B. Devale and Dr. R.V. Kulkarni discussed how the data mining method
performs sophisticated classification and correlation between policy designing andpolicy selection [1]. They presented association rules for determined the frequent
itemsets based on predefined support like riders that are often sold together with basic
products. They used market basket analysis to determine the association between
different policies that are sold for different purposes. In a part of classification
algorithms, A.B. Devale et al. presented the classification method of K-nearest
neighbors. It was used for classifying product selection in each age range andoccupation, providing big pictures of how to benefit from K-nearest neighbors by doing
segmentation based on income, paid premium, premium mode, and net of assurance
price. They showed many examples to classify the product type in the relationship ofeach feature above, such as “Life security,” “Tax benefit,” “Investment” [1].
Nan-Chen Hsieh & Kuo-Chung Chu used K-means to classify customers into
five clusters. It then applied the apriori algorithm as association analysis for eachproduct group in these customer clusters [55]. This experiment investigated whichfunction is proper to the customer's needs in a product of life insurance by extractingfrom knowledge rules of customer behavior and demand chain. Generating new productneeds customer data and risk rate, ridership, and ridership was modeled based onlimited data. The primary purpose is to share the risk and reduce premiums; after
adding expense and profit, a final premium is determined with more accuracy.
To improve product recommendations' performance, we can utilize the benefit
by integrating knowledge from the customer model to the product model to improve
this process. Knowledge Discovery in Databases (KDD) process in data mining canmerge customer data from different platforms. A.B. Devale and Dr. R.V.Kulkarni
presented the statistical principles that can be enhanced the capabilities of the data
mining method to improve directly into insurance procedures [1]. For example, thePearson correlation of statistical methods to a specific correlation coefficient in positive
and negative correlation was used to determine a correlation between policy designingand policy selection.
2.5.3 Risk Assesment
Risk assessment is one of the core functions of the insurance business. It
estimates the likelihood and impact of risk to represent the different levels of impact
with this risk. Insurance industries estimate and set the risk rating to predict the loss
from accidents, health claims, or disaster rates to support the underwriting process. It
set up the risk rating of the customer by placing the customer in a predetermined risk
class with ambiguous names such as “super preferred,” “preferred,” “standard,” and“substandard.” This rating is estimated from customer health and physicalcharacteristics (such as age, gender, height, weight, alcohol, drug, tobacco use, or
extreme sports). This rating will directly impact the premium rate that a customer must
pay to make a contract with an insurance company. The traditional method made use of17

actuarial statistics within the difficulty to predict base on stochastic. It is necessary toconstruct a risk prediction model from some techniques. Data mining and various fields
of machine learning, statistics, and optimization techniques are utilized.
In this section, we give some examples of previous works from various kinds oftechniques. The specific analytical techniques from data mining, such as market basket
analysis, clustering technique, and statistical method, can improve the fraud claim
process. IBM T.J.Watson's research center [14, 19] also developed a method called the"ProbE" (probabilistic estimation) predictive-modeling algorithm. This method uses
C++ kernel rule-based models of insurance risk, where each rule represents a risk
group. The purpose of this method is an analysis the competence of profitability using
property and casualty insurance policy for underwriting unit. Then use it to create thepredictive model for insurance risk. In the real work of actuary, the measurement ofstatistical confidence use to be an estimated risk parameter that can deviate from actual
values. In this experiment, there are concerning points about avoiding over-fitting. It
occurs when the best model of training data set perform worse when generated withnew data set.
Jianbing Xiahou and Yang Mu presented the comparable result between
traditional approach and data mining approach [37]. Traditional method is statisticalusing generalized linear models (GLMs). It is an extension of general linear function Y= XTβ + ε which consists of three factors: random factor means Y follows a normal
distribution,  = Y; system factor, namely  = XTβ (XT is transport matrix of X);
connection factor, namely h=m. In practice, Y does not follow a normal distribution.
Researchers also comment on the general statistic method in this experiment. This taskneeds to process data several times to fit in practice since the section of risk variables
and classification of variables are not very exact. That means the cost and complexity
are huge because the input matrix is huge.
Moreover, the model obtained from the fitting may not converge. They also
compared the experiment results to select the risk in classification using a decision tree
algorithm to rate making into risk analysis. They expected decision tree classifier could
solve the multitudinous problem of information classification. However, thisexperiment's results still did not satisfy since they found risk variables that make animportant impact on rate making are too few in their models. In addition, the risk factor
provided by the insurance agent was not reasonably rated. Yu Yan and Haiying Xie
explained the usefulness of data mining in dividing credit customers into several
grades, predicting customer risk, making credit ratings, and avoiding service risk [98].
They also explained in part of classification and prediction to construct a forecastingmodel of the future tendency of data using a decision tree, neural network, andBayesian network.
2.5.4 Data mining challenging for knowledge extraction in insurance businessKnowledge extraction efficiency depends on the quality of data that needs all
preprocessing KDD processes, especially cleansing data to handle missing data,
reducing noise, and ac-counting for time series. However, we still face some obstaclesof high volume and complexity of data. Many researchers are now interested in solving
a problem with data mining hybrid techniques to get high-performance results.

18

High volume and high complexity in the different kinds of data

There are different kinds of data types used in the data mining process, such as
structural and unstructured data types. Structured data is easily searchable with simple,
straightforward search engine algorithms, while unstructured data is the opposite. Alldata types are collected in different repositories such as relational databases, time-series
databases, data streams, or multimedia databases. We need to integrate all data
platforms into the same platform before extract knowledge by data mining technique.
Most of the historical data in a company is structured data; however, the data has anenormous data stream volume. The unstructured data also plays essential roles now
because some data in the company is a narrative platform. There are plenty of freetexts, such as customer comments retrieved from various websites. The unstructured
data needs some necessary steps to transform unstructured data to be structured data
before performing data mining by some techniques such as natural language processing(NLP) and human language technology (HLT).
Pennock et al. performed a semantic classification of reviewing messages in
unstructured data using N-gram's natural language processing. Then be continued data
mining processes to compare the results between support vector machine (SVM) andNaïve Bayes from C|Net text corpus [22]. Hu & Liu extracted the co-occurring sets ofterms from unstructured data. They build feature from these series of events and use
linking rule mining to find all common sets of items. In this context, a list is a set ofwords or phrases that occur together. They used the apriori algorithm to find all
frequent itemsets by satisfying user-specified minimum support; however, they did notconstruct any sequent patterns. In sum, the process of unstructured data utilizationneeds more complicated methods such as NLP, IR to preparing data before extract
knowledge from data mining algorithms [11, 31, 32].
Hybrid techniques in data mining research

To make the data mining method more powerful, many researchers presented
novel methods by consolidated data mining algorithms mixed or consolidated withother knowledge such as artificial intelligence, information retrieval (IR), and NLP. For
example, Nan-Chen Hieh & Kuo-Chung Chu presented the topic of enhancing
customer behavior analysis by data mining techniques [55]. This research paper showeda two-stage framework of consumer behavior analysis. The key feature is a cascade
involving a self-organizing map (SOM), a neural network, and a decision tree inducer.
This hybrid framework used clustering techniques to preprocess input samples into
homogeneous clusters and decision tree techniques to build a customer profile. This
paper did the clustering customer behavior from general demographic information suchas re-payment cycle, number of purchases, card age month, block code, sex, and credit
line. They gave some suggestions about the clustering algorithm that affects theperformance of the clustering result. The quality of input samples leads tomisclassification.
Shu-Hsien Liao et al. investigated which function best suited the needs ofconsumers in life insurance products, drawing on the form of knowledge and specificrules from consumers and the demand chain [72]. This research used the Apriori
clustering algorithm to illustrate the marketing segmentation and demand chain on life
insurance in Taiwan. They also did a segment the potential of insurance buyers into
similar groups using K-means and association rules based on their previous purchase.
192.5.5 Conclusion of data mining techniques in life insurance
We investigated the knowledge extraction from data mining techniques used forlife insurance. The primary purpose is to understand the current customer behaviors andutilize them to recommend a suitable policy to new customers to gain their highest
benefits and satisfactions. The company also has efficient customer relationshipmanagement and gains a higher persistence rate. Various data mining methods wereused for knowledge extraction. Each method has different strengthen points to utilize.
In this paper, we gave some examples of data mining methods to give some ideas toutilize them.
Furthermore, we found that Furthermore, we found that many researchers areinterested in hybrid techniques to make knowledge construction more efficient. The
correctness of knowledge extraction from data mining needs to overcome the obstacle
from high volume and high complexity from the different data sources, such as
structured and unstructured data generated from the diverse medium.
2.6 Life Insurance and Sentiment Extraction
2.6.1 Sentiment Analysis
Towards this, sentiment extraction is one of the main processes in the social
media utilization of the social CRM concept. Theory and previous work of sentiment
analysis have been reviewed. In recent years, sentimental analysis or opinion mining
has been widely used to understand how people feel about a product, a service, a person,or an object of interest. Sentiment analysis or opinion mining is also known as
sentiment extraction and affective rating (Binali et al., 2009). Sentiment analysis isdefined as determining subjectivity in sentence and orientation in terms of negative,
positive, or neutral based on the polarity strength (Binali et al., 2009).
In recent years as a sentiment analysis information extraction application, it has
been widely applied to the voice of customer content such as reviews and survey
responses, online and social media. It has been shown to be difficult for human readers
to find relevant resources, extract relevant sentences with comments, read summaries
and organize them into usable formats. This limitation triggers research on theautomatic sentimental analysis or opinion mining to understand how people feel about aproduct, a service, a person, or an object of interest by extracting opinions or humansentiments from texts [11, 32].
Nowadays, many researchers are focusing on sentiment analysis widely. The
general architecture process starts with identifying a categorization of opinion
sentences into positive or negative sense and summarizing the results. The knowledge
is then extracted from the assessment text consisting of different validation format that
may require different techniques to perform characterization. Attributes of a subject
element are tried to understand whether it is positive, negative, or neutral. Pang and Lee[2004] describe that opinion mining's primary function is to extract the opinion or
human sentiment based on texts from a document analysis process. The text of theworld can be divided into two main categories: facts and opinions. Facts are objective
expressions about entities, events, and properties. Opinion is often a subjective
expression that describes people's feelings, assessments, or feelings towards entities,
events, and properties. [Bing Liu, 2010] [48]. This thesis focuses on opinion
expressions that express positive or negative sentiments under CRM features.
In many cases, comments are hidden in long forum and blog posts. It is difficult
for a human reader to find relevant resources, extract relevant sentences with
20

comments, read summaries, and organize them into a usable format. Therefore, it isimperative to have an automatic search and summary system. Sentiment analysis, also
known as opinion mining, has grown from this demand. It is a natural language
processing issue or challenging text mining. (Bing Liu 2010)[48]. In general, sentiment
analysis was reviewed mainly at three levels: Document-level: The task was to classify
whether all comment papers expressed positive or negative feelings. (Pang, Lee, andVaithyanathan, 2002; Turney, 2002). Sentence Level: The work fits into a sentence anddetermines whether each sentence expresses a negative, positive, or neutral feeling.
Usually, neutrality means no opinion. This level of analysis is closely related to thesubjective classification. (Wiebe, Bruce, and O'Hara, 1999) [7, 83], which distinguishes
sentences (called objective sentences) that express factual information from sentences(called subjective sentences) that express subjective views and opinions. Aspect Level:
Both the document-level and sentence-level analysis did not discover what people likeand dislike. Aspect level, do a detailed analysis. Previously, aspect level was calledfeature-based opinion mining (Hu and Liu, 2004) [31,32]. Instead of concern withlanguage construction (clauses or phrases, sentences, paragraphs, documents), aspectlevel directly points at its opinion. It is based on the idea that an opinion consists of asentiment (negative or positive) and a target (of opinion).
In sentimental analysis and opinion mining, four major steps are(1) Opinion identification, In the step of opinion identification, since opinions inmany cases are hidden in long posts and blogs, the model needs to identify whether asentence or a part of a text is a fact, opinion, question/answer, advertisement, or othertypes. Typically, two main types of information in texts are (1) facts (objective
expressions) related to entities (objects, events, and their properties) and (2) opinions(subjective expressions) describing one’s sentiments or feelings on entities; even otherinformation types include questions, answers, advertising and so forth [10].
(2) Polarity recognition, once a sentence was recognized as an opinion, we need todetermine whether the opinion is positive, negative, or neutral for polarity recognitionin the second step.
(3) Target identification, as the third step, is known as aspect-based or feature-based
sentiment analysis [16] target of the opinion needs to be identified. The target can be an
object, an individual, an organization, an event, a topic, a product, a service, or/and its
associated component, attribute, and feature.
(4) Aspect or feature-based summary has to be generated from sentiments or
opinions in multiple sources. So far, several researchers have studied the problem ofgenerating feature-based (aspect-based) summaries from customer reviews of products
called FBS (Feature-Based Summarization), such as those in [10, 16, 69].
Opinion identification determines the semantic orientations (positive, negative, or
neutral) of opinions expressed in reviews' product features. There are many applications
to support this problem such as opinion mining, summarization, and searching
including the technique to utilize a list of opinion words (also called opinion lexicon)
for the purpose (Bing Liu and Xiaowen Ding, 2008) [76]. They offer an effective way
to pinpoint the meaning of opinions expressed by reviewers on product features. They
can deal with the semantic orientations independent context by a holistic approach andaggregate multiple opinion words in the same sentence. There are many researchers
study the generating problem of feature-based summaries from the product reviews
called FBS (Feature-Based Summarization), which has also been implemented(Minqing Hu and Bing Liu, 2004) [48].
21Previous works of Riloff and Wiebe have proposed different approaches forautomatically constructing the lexicons for the feature-based opinion mining [93]. Most
approaches applied some machine learning algorithms for learning the rules from thecorpus. In machine learning, it has often been practical to use labeled and unlabeled
examples in tandem, e.g., Nigam et al. (2000). Turney’s model introduces further
consideration of incorporating human-provided knowledge about language. This paper
was built models that utilize all three sources: labeled documents, unlabeled documents,and human-provided information. The basic concept behind Turney’s model is quite
simple. The “sentiment orientation” (Hatzivassiloglou and McKeown, 1997) of a pairof words is taken to be known. Generally, many papers presented the experiment inEnglish language corpus. Some papers presented the experiments in another language
corpus, such as to propose an unsupervised lexicon building method for the detection ofpolar clauses, which convey positive or negative aspects in a specific domain (Hiroshi
Kanayama, Tetsuya Nasukawa, 2006) [97]. Their lexicon can be expanded
automatically using un-annotated corpora, and tuning the threshold values is not
required. They used Japanese corpora from discussion boards. Some approaches
employed morphological structures of words to extract opinion words in the Chinese
language instead of the bag-of characters approach. (Ku et al., 2009).
2.6.2 Sentiment Analysis in Thai Life Insurance
While insurance companies collect large volumes of text daily through theiragents, customer service centers, emails, social networks, online web communication,and so on, most cannot handle, classify, interpret or extract the essential informationfrom such materials efficiently. As precious sources for customer analysis, suchinformation includes policies, claims, and complaints, results of surveys, expert andhealth reports, relevant interactions between customers and non-customers (prospect) insocial networks, etc. The insurance industry is among the ones that most can benefit
from the application of technologies for the intelligent analysis of a free text (known as
Text Analytics, Text Mining, or Natural Language Processing). Insurance companies
have to cope with the challenge of combining the results of the analysis of these textual
contents with structured data (stored in conventional databases) to improve decisionmaking. In this sense, industry analysts consider essential the use of multiple
technologies based on Artificial Intelligence (intelligent systems), Machine Learning(data mining), and Natural Language Processing (both statistical and symbolic orsemantic). The customer experience is a key element in the commercial success in theinsurance sector, where differentiation between the insurers’ products is not easy.
Companies are trying to know their customers and their opinions by employing
satisfaction surveys and directly through social networks. Text analytics permits toclassify interactions according to the products or services offered, the marketing
channels used, the operations employed, etc.
Besides, automatic opinion and sentiment analysis techniques enable identifying
the polarity (positive, negative, or neutral sentiment) about issues or specific aspects ofa product, channel, or procedure. When this type of analysis is applied to comments inopen social networks, it is also possible to detect trends in the sector and identify brandperception (which concepts, activities, or entities are associated. And how we
differentiate ourselves from competitors), qualify our company's corporate reputation or
brand, or provide early warning of potential reputational crises. The activities that have22to do with market research and competition analysis are essential to determining its
product strategy [62].
To make high benefits in the real world by present the sentiment analysis results
related to business topics and measure the subjectivity for giving a score on eachsentiment term to show the severity of each topic detail. A company can understand thecustomers' minds and gain insights into its strengths and weaknesses. Customer
satisfaction is one critical success of a business, so understanding customer minds ineach business function are needed. Our previous research declared the group of analysis
tasks as the essential functions discussed widely in the criticized website likepantip.com into three main analysis modules. First is intention analysis, second is topic
analysis, and the last one is sentiment analysis.
1. Intention analysis consists of two categories- Questions intention: we consider this category to express customers' real needs
that are faced with insurance problems or asking for information related to products andservices.
For example, "ต้ องการทาประกันชีวิต แบบสะสมทรั พย์ให้ ลกู ขอคาแนะนาด้ วยค่ ะ," "I would like tomake a new endowment insurance contract, please recommend."- Informative intention: this category refers to messages in which users provide
knowledge or useful information to society. For example, "ประกันชีวิตนาไปลดหย่อนภาษีรายได้ ส่ วนบุคคลได้ หนึ่งแสนบาทต่ อปี ," "Life insurance premium can use for personal income tax refund
100,000 baths per year".
2. Topic analysis: This analysis's main purpose is to show demands orawareness in these topics for businesses to solve problems in time. It consists of three
categories.
- Life insurance products: There are many kinds of life insurance products. Allof them have many conditions for different purposes. The example of a basic product indifferent purposes such as
An endowment is periodic insurance providing for the payment date beyond thematurity date of the policy.
Whole life insurance is permanent insurance and provides lifetime coverage.
Term life insurance is for a limited time, such as ten years etc.
Universal life insurance is flexible and allows increasing premiums over time.
--

Regulations: This category refers to messages or posts related to rules andconditions under life insurance concerns. All of the issues affect business units
in each operation, such as the set of regulations about life insurance policy issue& rejection, the regulatory rules of claim procedure, etc.
Service Centers: High customer satisfaction is gained from high-quality service
from service centers. So this category consists of various criticized contentsfrom a customer related to selling agents, call center, or other service centers.

3. Sentiment analysis: This intention shows the sense in customers' minds towards
customers' intention related to each analysis topic. It has a positive and negative
sense.

23

2.6.3 Hierarchical Aspect-based Sentiment Analysis
In previous work of aspect-based or feature-based sentiment analysis, there is anattempt to find out the targets on which opinions have been expressed in a sentence.
Some researchers studied the summarization of generating feature-based of customer
products-reviews called FBS (Feature-Based Summarization), which has also been
applied [48]. In this research, we also study the CRM conceptualization to identify
aspects and sub-aspects to support the business structures/process.
An important task is how to set up the granularity of sentiment level; thepolarity scoring is identified. In the real situation, many words' polarities depend on the
aspect (Li, Huang, and Zhu 2010), and the differentiation of prior and contextual
polarity is crucial [42]. For example, 'Slow', 'Fast' words are showed in a different
aspect. They may lead to be different sense and also impact the polarity of that word.
Not only 'adjective' or 'verb' can declare the polarity of sentiment, but 'noun’/’pronoun'or phrase can represent the sentiment word's polarity. So in our text mining task, weutilize the Thai standard dictionary and construct a special dictionary to track thesemantic of words or phrases. However, one of the most challenging issues is how tohandle sentiments in several aspects in a concise, systematic, and comprehensive way.
In recent years, there is suddenly increase the interest in aspect-based sentiment
analysis [42]. However, less paper is concerned about the structure of the aspects.
Significantly, the aspects identifying under business knowledge need the layer of abusiness function to clarify each object’s sentiment clearly because the components ofaspect also have a relationship with sub-aspect components. For example, social CRM
conceptualize components have main functions: People, Process, and Technology.
What are aspects or sub-aspects under People, Process, and Technology should be
declared?
From the viewpoint of technology, the hierarchical such tree structure can
support sentiment extraction. Well-identified sentiment words contribute much to theaccuracy of sentiment analysis. The multidimensional concept and its extension tocover online analytical processing are interesting in representing information in an
abstract and implementation-independent way. There is some research related tomultidimensional sentiment analysis, such as Suin-Kim et al. purpose a hierarchicalaspect sentiment model (HASM) to discover a hierarchical structure of aspect-based
sentiment from an unlabeled online review. They use a tree structure to represent tohelp the user quickly understand the significant opinions from massive online reviews[42]. Steven C. Harris et al. presents the development and initial results of a uniquemultidimensional sentiment analysis agent for an online learning environment toprovide overall student feedback. However, this area's research still goes on because it
is not easy to jointly model aspect hierarchical structure and the aspect-sentiment topics
from an unlabeled corpus. [42]
Our research declares an aspect-based sentiment lexicon using aspects in theThai life insurance industry similar to the above details and supplementing more details
in the life insurance business process related to CRM functions. Then merge thesentiment analysis concept into our new approach using a multidimensional sentimentcube model to manage and visualize relations among factors (aspects/sub-aspects in ahierarchical form). A set of suitable dimensions is designed to consider standard CRM
factors for the Thai life insurance industry on aspect-based sentiment analysis.

24

2.6.4 Sentiment Analysis Scoring
Taboada's previous task presents an approach to extract the most relevant
sentences and calculate semantic orientation weighting using the SO-CAL (Semantic
Orientation CALculator) concept. This task also followed Turney's basic approach to
calculate orientation experimenting with the web-based search context [50].
2.7 Sentiment Extraction with Unstructure data
2.7.1 Text Mining and Natural Language Processing (NLP)
Text or message analysis that the computer is trying to understand the nature oflanguage applied knowledge in many fields such as knowledge discovery or data
mining and natural language processing (NLP), etc. (Andreas, Gerhard, Fraunhofer andSankt, 2005). The process of knowledge extraction from large textual information to
gain useful textual information using comprehensive information such as text data sets
or natural languages is called text mining, where knowledge is acquired. In terms oflatent data search, it is used in the analysis [41].
The characteristics of the data for analyzing latency separated into two types are:(1) Structured data: It is called data mining analysis by data that have patterns.
(2) Nonstructural or unstructured data: Unstructured or implicit structured data,
most often in the form of text or natural language, is called text mining analysis. Or theknowledge discovery process from Text (KDT).
For the analysis of the message, there is a process different from text mining. It
is a structured or unstructured structure. Therefore, the process of message analysis isnecessary to convert the unstructured message to be structured. Ronen and James,
2007) are called the data preparation process. The data preparation process is anecessary process that is time-consuming to obtain the appropriate structured data. This
point is also different from the data mining algorithms for analysis purposes. A
preparation process involves Natural Language Processing Techniques, which helps
prepare the message for the computer processing for more understanding of words andsentences in natural languages. The result of the searching of latency information isknowledge or useful information.
Therefore data mining will be applied with the natural language processing (NLP)
and text mining theory to improve algorithms. Data mining process consists of mainprocesses as below:
1. Data selection
2. Data cleansing
3. Data constructing (data population)
4. Data Integration
5. Data Transformation
For the text mining task in this research was processed with NLP task, we classified
the data preprocessing into three processes.
1. Feature selection
2. Text cleansing
3. Text representation
Techniques for explaining and predict data model consists of two main techniques.
1. Supervised learning such as text classification or text categorization
2. Unsupervised learning, such as clustering technique.
25

Model evaluation from data mining required four processing theories, which composeof 1) Text representation, 2) Natural language processing, 3) text classification and textclassification evaluation as details below.
2.7.2 Text Representation
Unstructured data analysis needs to be transformed into a structured format forcomputer processing. The text representation process makes a substitute for a message
in the form of a Vector Space Model (VSM) that is a method to represent a message interms of feature vector space. It can choose text attributes to represent in several ways,
such as words, phrases, or parts of speech (POS). Usually, text representation analyzes
based on words called (bag-of-word) substitute for every word in a document with avector consisting of a member of the value representing the word within the message.
The method to represent the value representing the text message is to calculate thevalue for defining the message. (Atorn Numtiyagul, 2006).
- Word replacement with a occurred value or not occurred value (Binary
weighting)1o Binary weighting =-, otherwise{ , for term represent in the document0

Term frequency (TF)
Term Frequency – Inverse Document Frequency: TF-IDF
o TF-IDF is the most popular way to represent text. This is because it is a
simple and high efficient way of calculation result.
Term Frequency – Inverse Document Frequency (TF-IDF)

Salton and Buckley (1988) propose the method for text representation with tf-idf which
is word frequency calculation with weight of word (wd) calculate from tf multiply with
idf value|D|
wd = fw,d * log()
fw , dwhen fw,d is tf (term frequency) which is frequency value of word (w) in
document (d) multiply with logarithmic scale of number of document (D) which divideby the number of documents are found words (w). It means the inverse document
frequency : idf (Juan, 1999).
However, this method has a limitation of evaluation performance for documentclassification. Because some words inside documents are not a good representative of adocument, “stop word removal” and “word stemming” are methods for classified word
qualification; these methods can increase text classification efficiency. Moreover, it can
reduce document volume down 30-50%.
- Stop word removal is the word that has a high opportunity to occur in the
document and does not useful for text classification such as conjunction,
preposition, ending word, etc.
- Word stemming is the word that has the same root word; however, the pattern ischanged, such as "eat, ate, eaten". They have the same root word, "eat".
However, the Thai language does not have that word characteristics.
Nevertheless, the Thai language has the same word pattern in different meaning26

or different word patterns in the same meaning, such as ‘กิน/eat’ and ‘รับประทาน/eat’.
Vector space model that uses single or phrase of words to represent a word in
documents has many previous works proposed the techniques about feature selection byconsidering the word occurrence such as information gain (IG), mutual information(MI), chi-square, etc. Minqing Hu and Bing Liu (2004b) proposed the method that uses
association rules mining for analysis of the frequency of the relation among features
start from two words co-occurrence onwards [31].
2.7.3 Association Rules Mining
Association rules mining is a rule to finding out the relationship of data morethan of equal two sets within the same big group of data. This technique was proposedby Rakesh Agrawal and Ramakrishnan Srikant (1994). One of the useful techniques ofassociation rule mining is the ‘market basket.’ The process that constructs the data
relationship rules separate into two processes: 1) Finding the frequency item set andmeasuring the relationship among features with ‘support’ values. If the association ofsome data sets has a support value more than the ‘minimum support’ value called‘Frequency pattern’ or ‘Frequency item set.’ The next process is ‘Association rules’
identification, which is a process using the association under minimum support value toconstruct the rule. If some of the association of dataset has ‘confidence’ value morethan ‘minimum confidence’ which is limited called ‘Association rule.’Frequency
ItemsetItemset

Association
Rule

Minimum
Support

Minimum
Confidence

Figure 3: The process of association rules mining
Support is an indication of how frequently the item set appears in the dataset. (Kenneth
and Narciso, 2007)transactions_ contain _ AB
Support (AB) =total _ transactions

Confidence is an indication of how often the rule has been found to be true (Kenneth etal., 2007)
Support( AB )Confidence A  B =Support( A)
2.7.4 Natural Language Processing (NLP)
Natural language processing (NLP) is one of the most important information
technologies which try to understanding complex language utterances is also a crucialpart of artificial intelligence. Human language is used to communicate with a computerin a structured language that can be processed immediately, such as PHP, Java, or C++. However, human language has its structure called natural language, which is an
unstructured structure.
27

Natural language processing is divided into the following analysis levels:(Christopher and Hinrich, 1999)
1. Morphological analysis is a word analysis level that makes use of Lexicon
knowledge.
2. Syntactic analysis is Part-of-Speech of words analysis level, which is the basic
information for examining language's grammatical rules.
3. Semantic analysis is an analysis of the meaning of words in a sentence.
4. Discourse integration is considered the meaning of a sentence with an adjacentsentence.
5. Pragmatic analysis is the interpretation of Sentences in which the speaker wants
to convey.
The language analysis process starts at the lowest level, morphological analysis(word level) until pragmatic analysis can explain the language structure elements. Thatis word, morpheme, phrase, noun phrase, verb phrase, sentence, and grammar. An
analysis in higher-level more than pragmatic requires a knowledge base of a word suchas WordNet. The Thai language is frequency analysis in morphological analysis andsemantic analysis because Thai WordNet is underdeveloped and has some limitations(Alisa et al.m 2010) [17]. Morphological analysis and semantic analysis in Thai
language analysis have more complicated than in English. The Thai language does nothave any stop wording in a sentence, including writing style is to continue until
becoming one passage. Therefore the process of managing the Thai language before
analysis requires three necessary steps: 1) Tokenization, 2) Part-of-Speech tagging, and
3) Syntactic analysis.
2.7.5 Word Segmentation
Research about word segmentation is separated into three techniques. There is1) a Rule-based approach, which is a method considering character or alphabet. Thismethod is the most convenient and fast; however, it cannot solve ambiguous words
problem. 2) Dictionary approach: This method makes a segment of a word using‘longest string matching’ with ‘maximal matching.’ This method is more accurate than
the rule-based; however, it still cannot solve all of the ambiguous word problems. 3.)
Corpus-based approach: this approach use statistic to calculate the probability of word
occurrence. This method gets the most accuracy; however, there is a limitation based on
the accuracy of corpus database.
2.7.6 Part-of-Speech Tagging (POS tagging)
Part-of-Speech tagging in the level of syntactic makes a better understanding of
the sentence meaning. This process makes specific the function in each word that
present in the grammatical pattern. The word's functions consist of N-noun, PRONpronoun, V-verb, AUX-auxiliary verb, ADJ-adjective, PREP-preposition CONJconjunction, DET-determiner, CLAS-word class, NEG-negative/reject the term, ENDstop word.
2.7.7 Grammatical Syntax Analysis
Syntax or Parsing is the process that explains the sentence structure with
Grammar formalism to define the word pattern in a sentence. It describes in a Tree28

diagram and Labeled bracketing. One of the popular gramma formulas is ‘Context-Free
Grammar: CFG). Context-Free Grammar is one of phrase structure grammars. PS
which does not concern the word meaning but consider in the ordering of word function
from left to right and split step with two symbol types 1) Non-terminal symbols such as
Part-of-Speech (POS) or chunks 2) Terminal symbols refer to a word in a dictionary.
Generally, the Thai language has a lot of ambiguous meaning and conversational
language in sentences. The challenge problems in the Thai language are managing thelong sentence and separating wording with the correct definition. The Thai language isthe one Asian language has difficulty in clarifying the end of each sentence. Many
papers about Thai language classification present in terms of the word segmentation byusing the dictionary base. This method may face with the ambiguity of the word. So
another technique to do word classification is using statistics base on the various corpus.
Asanee et al. proposed an approach for Thai segmentation by using decision tree
learning [4, 5]. The next research has the experiment one of closely related to this paper
is Constructing Thai Opinion Mining Resource: A Case Study on Hotel Reviews ofHaruechaiyasak et al. suggest a Thai language resource of constructing framework forfeature-based opinion mining that extracts lexicons, including domain-dependent anddomain-independent, based on syntactic pattern analysis. This experiment found thatpolar words could be extracted more easily than sub-features [36]. It may cause subfeature extraction, and polar words are not straightforward meaning owing to it is quitedifficult to deduce from sentences of the corpus. The other task of Haruechaiyasak et al.
proposes an S-Sense framework for analyzing sentiment on Thai social media on
intention, sentiment, and language usage in Thai texts [17].
Benea et al. (2008) is automatically generated resources for subjectivity analysis
in a new language using machine translation and standard Naive Bayes and SVM
leveraging on the resources available in English [88]. This experiment exemplifies thetechnique of Romanian and Spanish. In terms of negation in Sentiment Analysis, some
papers presented in the survey pattern find out how to solve the ambiguous meaning
problem in negative polar words (Michael Wiegand et al., 2010) [51]. This researchalso used an approach to exploit linguistic knowledge to improve topic and sentimentclassification in online opinions related to Thailand's life insurance business. Thisapproach automatically assigns appropriate labels on a proper class by training theclassification models and language resources with linguistic clues to extract sentimentfrom texts in section 3.
2.8 Online Analytical Processing (OLAP) & Multidimensional Sentiment Cube
2.8.1 Online Analytical Processing (OLAP) and Multidimensional Cube
Online Analytical Processing (OLAP) is one of the database technologies,
which has previously attracted many researchers' interest. OLAP is based on amultidimensional data view supported by a multidimensional database (MOLAP) or a
relational engine (ROLAP). The main characteristics of OLAP applications are (a) dataon multidimensional view and (b) Data analysis through queries to guide the data
(Panos Vassiliadis, 1999).
The multidimensional data view recognizes which information is contained in a
multidimensional array (or Hypercube, cube). A cube is a collection of data cells
organized by dimensions. Dimensions are defined as the features structure. Eachdimension has an associated hierarchy of levels of user's analysis viewpoint. For29

example, the time dimension can be derived as Year, Month, Week, and Day as ahierarchy. Measurement is variables, metrics, or facts which stand for themeasurements value [90].
The traditional multidimensional model is amultidimensional fact-based structure generated from complex queries [39]. A fact set
up from analysis viewpoints of some concerning events called 'dimension' (e.g., service
touchpoint, service process) can be quantified from measures called 'measurement' (e.g.,number of complaint words). The measurements can be aggregated into different levelsof concepts across different layers of multi-dimensions as hierarchical form. A data
cube provides multidimensional perspective viewpoints though multi-measurements
combination to multi-dimension values called 'multidimensional cell.' An analysis pointof view can vary by typical OLAP operators such as 'drill-down,' 'roll-up,' 'slice anddice,' 'pivot' or 'top-k selection.'
2.8.2 Sentiment Cube
Recently, to catch the summarized figure of sentimental analysis, a sentimental
cube has been introduced by Umeshwar Dayal in 2012 [84] [85]. To combine
multidimensional cube with sentiment analysis theory, Umeshwar Dayal defined patent(US20120197950 A1) of sentiment cube that “A sentiment cube system is a disclosed
system which stores sentiment elements inside. A sentiment cube data structure ishaving a set of cells arranged by a set of dimensions. The system includes a computer
programmed with executable instructions that operate a set of modules. The modules
comprise a sentiment storage module that receives sentiment values associated with aset of entity features and then populates a hierarchy of the cells in the sentiment cubewith the sentiment values. Sentiment analysis modules affect a set of operations on thesentiment cube [85]”.
Sentiment cube emerges in the era of social media, which raise sentiment
analysis techniques. The sentiment is deployed to be one of the analysis dimensions.
That makes it more potent on analysis viewpoints. Start from the definition of asentiment cube system, this study illustrates our proposed design of a sentiment cube
for Thai life insurance business using the social CRM and its practice use with threemain properties, i.e., multi-dimensions, hierarchical dimensions, and multimeasurements. A sentiment cube system is proposed to store sentiment elements inside,
together with their target aspects. Its data structure consists of a set of cells, organizedin the form of dimension structure. The system keeps sentiment values associated witha set of entity features (aspects) and then populates a hierarchy of the cells in thesentiment cube with the sentiment values. The system allows a set of operations on thesentiment cube, such as drill-down, drill-up, slice/dice, pivot, top-k selection, etc.
Previous work-related to the multidimensional cube on unstructured data, many
researchers started the experiment on unstructured data such as text documents, chatblogs on Twitter, etc. For example, Frank, Olivier et al. presented the OLAP
multidimensional concept model without facts. This model is based on a specificdimensions concept and is used for the analysis of multidimensional documents. They
also provided the operation set of cube explanations [69]. Xiong Liu et al. discussed atext cube approach to studying different kinds of human, social and cultural behavior(HSCB) embedded in the Twitter stream. They showed how to organize data from text
to multiple dimensions and hierarchies and make visualization with statistical reports
and perform online analytical processing [2]. Duo et al. proposed the combination of
topic cube and probabilistic topic modeling by enable OLAP on the dimension of30

multidimensional text database. They also presented two heuristic aggregations toaccelerate the iterative EM algorithm by leveraging the models learned on component
data cells in a good starting point of iteration for topic models estimation.
2.9 Customer Satisfaction in Life Insurance
There are many studies related to customer satisfaction in life insurance withdemography concepts as follows:
Demography Characteristics Concepts
Demographic characteristics include age, gender, family size, family status,
income, occupation. These studies are criteria that are commonly used in market
segmentation. Demographic characteristics are important characteristics andmeasurable statistics of the population that help determine the target market, including
easier to measure than other variables. With different demographic characteristics
having different psychological characteristics, the important demographic variables areas below
1. Age is a factor that makes people different about thoughts and behaviors.
Younger people have liberal ideas. Adhere to ideology and look more optimistic
than those who are older. Many older people have conservative ideas adhering tocautious practices, more pessimistic than younger people because of their different
life experiences. The product will meet the needs of varying age groups; marketers,
therefore, take advantage of their age, which is another demographic variable of themarket segment.
2. Gender is also an important variable in market segmentation. Gender variables
have changed in consumption behavior, especially women. Gender differences
make people have different communication behaviors; females tend to receive moremessages than males, while the male desires to create a relationship between
sending that message. Also, cultural values and attitudes define the different rolesof both sexes.
3. Education is a factor that makes people have different ideas, values, attitudes,
and behaviors. Highly educated people have a significant advantage in being
suitable recipients because they have a good understanding of communication andconsider enough reason. In contrast, people with low education tend to find nonradio, television, and print media.
4. Social and economic status means occupation; the person's income and social
image influence the message recipients. Each person has a different culture,experience, values, attitudes, and goals.
Attitude Concept:
Attitude refers to assessing satisfaction or dissatisfaction of a person, emotional
feelings, and practical trends that affect a particular idea or a thing or referring to aperson's feelings for something. Attitude is an influence on faith. At the same time,
belief has an impact on attitude. The attitude is caused by the information that each
person receives from the experience about the product or the person's mind, including
the relationship with the reference group, such as father, mother, friend, a leading
person in society, etc.
Experience Concept:
31

Experience means an experience that has been caused by an action or seen.
Experience is valuable in learning every aspect of the experience that affects thecreation of art. There are two types as follows: 1) Direct experience is an experiencethat we have encountered or touched by ourselves, found ourselves, acted ourselves,heard and listened to ourselves 2) Secondary experience or also known as "Indirect
experience," is another experience that has been inherited or acknowledged.
Customer Satisfaction Concepts:
Customer satisfaction is one of the critical factors that will make the company
successful. Making customers satisfied is essential because the advancement ofservice is a crucial factor. If the number of additional users increases, customer
satisfaction will make the business gain more market share. The high volume ofpurchase repeatedly is referenced business that will lead to better profitability(Barsky, 1992). Person (1993) has given meaning to customer satisfaction; that is, thelevel of customer feelings toward products or services, whether the product or service
can meet the needs of the customers' expectations. It gets better customer satisfaction
and loyalty to products and services, causing the behavior of repeat purchases oradditional services. Moreover, customer satisfaction will make the customer tell theclosed person, such as family and friend.
To summarizes previous work about customer satisfaction. Many previous
studies have focused on the indicators that influence customer satisfaction in life
insurance. However, these studies had their objectives. For example, Kuhlemeyerand Allen (1999) explored consumer satisfaction relevant to the purchase behavior oflife insurance products and compared the service provided by a broker or agent toservice with no broker or agent support [43]. They attempted to identify a benchmark
for customer satisfaction for life insurance products, agents, and providers. Sogunroand Abiola (2014) studied the measurement of customer satisfaction among multiattribute products and services based on product purchase in Logos State, Nigeria.
Their study recommended that life insurance service providers in Nigeria continue to
conduct customer surveys on their products to identify each product's aspects thatcreated dissatisfaction [75] (Sogunro & Abiola, 2014). Subashini and Velmurugan(2016) studied policyholder satisfaction with various factors relating to life insurance
products in Coimbatore District, India. They chose this area because insurancecompanies were facing a significant problem of lapsed policies [78]. Some
researches highlighted the importance of customer satisfaction on the service qualityof life insurance; for example, Siddiqui and Sharma (2010) administered aquestionnaire survey using confirmatory factor analyses by investigating six
dimensions of service-quality: assurance, competence, tangibles, personalized
financial planning, corporate image, and technology. They used structural equation
modeling to assess the results and proposed a framework for appropriate action tosatisfy customers through quality services [73]. High customer expectations in acompetitive business environment led Samarasinghe et al. (2018) to focus on
customer satisfaction in terms of service quality in Colombo District, Sri Lanka.
Their research investigated the factors influencing the satisfaction levels ofcustomers [71].
Kannan (2018) also studied customer satisfaction with corporate life insurancewith particular reference to Chennai's city, in India. The main aim of that study wasto discover customer satisfaction with life insurance companies based on primarydata from a questionnaire survey of 150 policyholders. They concluded that insurers
32needed better understand the customer requirements for such policies and increase
promotions, such as advertising. Besides, greater awareness was needed among
illiterate and rural groups [40]. Nguyen et al.(2018) presented the determinants ofcustomer satisfaction and loyalty in Vietnam's life insurance industry based on aquestionnaire survey on factors that would enhance customer relations, image,quality, and added value to create a sustainable life insurance business in Vietnam[59]. (Nguyen et al., 2018). Basaula (2017) studied the awareness of customer
satisfaction with the claim settlement process in Nepal using questionnaires. The
results indicated that many respondents gave neutral scores on claim satisfaction.
They required faster claim settlement; further, the study stated that the government
should focus on creating life insurance awareness [8].
Many researchers have examined the significant indicators that affectcustomer satisfaction from various perspectives. Coviello and Trapani (2012)
proposed a conceptual for investigating the customer retention using the satisfaction
on customer relationship and quality. Their study looked at the relevant concept ofclient satisfaction derived from the aggregation of individual experiences in different
situations, including psychological states and customer attitude toward service as theimpact on satisfaction levels [21]. Bakar, Soykan, and Acar (2018) measured life
insurance knowledge among students of the insurance and risk management
department at Dumlupinar University in Turkey. Although the research was not
related to satisfaction levels, it measured the students' basic life insurance
knowledge. It found that even those who wished to work in the insurance sector hadvery little knowledge of basic life insurance. This indicated the need for better
education and knowledge sharing with students. Further, it implied that insurance
knowledge would influence the life insurance market and, therefore, more knowledge
support was necessary [6].
Research on the life insurance market is also important in a developed country
such as Japan, as the country experiences depopulation due to aging and a declining
birthrate. Thus, life insurance companies face a shrinking market. Tomoki Inoue (2014)
studied various topics relevant to customer behavior changes based on informationfrom the internet and found that the internet will impact growth in the life insurance
market. Therefore, it is important to increase our understanding of consumers' actual
actions, including factors and behaviors that affect customer satisfaction, considering
the increase in social media [36].
In addition, scholars have also investigated customer satisfaction in the Thai life
insurance industry. For example, Phromsuwan (2011) studied customer attitude andsatisfaction with buying life insurance from a telemarketing channel from a company
called "Siam Commercial New York Life" in Bangkok. That study focused on customer
demographics and attitudes toward the service provider [66]. Wongwiratchit (2015)
also studied customer satisfaction with life insurance in Thailand. The study evaluatedcustomer satisfaction using path analysis on customer expectations, perceived value,
perceived quality, and customer loyalty [94]. Other studies on Thai life insurance havefocused on satisfaction factors that impact life insurance purchases, such as
Chompuphan (2014), who studied factors affecting the intention to buy long-term life
insurance in Northeast Thailand [20].
In all, we found many studies on life insurance in emerging markets. Customer
satisfaction has been shown to depend on customer opinions in specific areas. Thus,
researchers have investigated customer satisfaction in many relevant areas, such aspurchasing life insurance products, service quality, customer loyalty, claim settlements,
33

service channels, and telemarketing. These studies have all examined influential factors
and customer satisfaction levels using questions based on service providers' expertiseusing inside-out knowledge. Our research utilizes outside-in knowledge by tracking
customer dissatisfaction expressed in messages on websites to extend a current
analysis. They are then analyzing the extracted knowledge for service providers‘benefit.

34

Chapter 3
Implementation
This section presents the background and idea of implementing customer
sentiment knowledge management and its process in Thai life insurance. The concept is
explained as a knowledge management process, including the implementation of support
tools; the multidimensional sentiment cube design is described and illustrates detailed
implementation, including information extraction and sentiment calculation. Besides, thesentiment knowledge sharing using a questionnaire is explained.
3.1 The Concept of Customer Sentiment Knowledge Management
Customer knowledge management is the process for managing knowledge related
to the customer, such as customer profile and customer activity. In the social media era,customers prefer to express feelings on social media, both positive and negative opinions.
However, a company can utilize and solve the problems in time with negative sentimentextraction. In addition, customer sentiment knowledge management is also theknowledge management that exploits the benefit from customer sentiment such as
product improvement strategy based on positive opinion, complaint management system
based on negative emotion.
Knowledge may arise from the transmission of experience or the analysis andsynthesis of information. Knowledge creation is set up from knowledge co-creation.
Knowledge on customer sentiment is the knowledge arises on the particular subject
related to customer sentiment on products or service. To manage the customer sentimentknowledge has significant to business sustainability because it raises customer
satisfaction.
However, extracting customer minds from messages spreading in social media is
not an easy task because it requires language and analysis technology based on businessknowledge. There is one concern from sentiment extraction from social media; it isentirely unclear a characteristic of a person who expresses that kind of sentiment onsocial media as anonymous sources.
Our methodology defines customer sentiment from the information obtained fromthe Outside-In and Inside-Out management approach of the organization. The Outside-In
approach is guided by the customer's experience and their opinion that customer value
creation, customer orientation, and customer experiences are the keys to success. The
value is a consequence of listening and providing value to customers. The Inside-Out
approach is guided by the practice, experience that the inner strengths and capabilities ofthe organization will make the organization prevail. The knowledge co-creation fromboth outside-in and inside-out information related to customer sentiment benefited
business, especially process improvement, product and service evaluation, etc. However,the utilization of that kind of sentiment knowledge requires knowledge management tomake it more powerful and practical in real practice.
The difference between Customer knowledge management and Customer sentimentknowledge management:35

1. Customer Knowledge Management- Disciplinary partners: customer relationship management and knowledge
management approaches.
- Perspective: the interface of customer and the inside of an organization.
- Key actors: customers and employees.
- Key communication context: collaboration between customer and organization.
- Conceptual focus: What is the customer knowledge from different sources andtypes?
- Key processes: utilize the customer knowledge of customer and organization.
- Goal: adapt the knowledge from customers and organization to support CRM
efforts.
2. Customer sentiment knowledge management- Disciplinary partners: CRM, knowledge management, and sentiment analysis
approach.
- Perspective: customer interface, inside an organization, and social media
interface.
- Key actors: employees, customers (anonymous), and customer (specific in
demographic profile).
- Key communication context: collaboration between customer-specific related tocustomer sentiment and the organization to increase customer satisfaction.
- Conceptual focus: What is the customer knowledge from different customer
sentiment?
- Key processes: utilize the customer sentiment knowledge on the organization byrequiring the transform process between customer sentiments to businessknowledge using technology.
- Goal: learning from customer and utilize in the organization to get process
innovation or improvement to increase customer satisfaction.
3.2 The Methodology of customer sentiment knowledge management

Figure 4: The methodology of customer sentiment knowledge management36Refer to the previous passage; our methodology defines customer sentiment fromthe information obtained from the Outside-In and Inside-Out management approach of
the organization to get knowledge for finding out the methods or processes to support life
insurance increase customer satisfaction.
The methodology starts from the outside-in approach of life insurance, which
expects to get customer sentiment in each service aspect of life insurance from social
media. Data collection from this process will get free-text from various critiqued
websites in life insurance service merge with the social CRM principle and life insurance
knowledge. In the data collection step, we will get data in text forms and require
technology such as text preprocessing, text mining, natural language processing (NLP),
and sentiment analysis to extract customer sentiment, (refer to Figure 4: Step 1 – Identify
and access). Next, Data analysis deploys the sentiment analysis concept for analyzing
customer sentiment results. In this step, we use the “Sansarn Tagging tool,” which helps
extract the seed of words (keywords) as a sentiment extraction tool. We propose a“Multidimensional sentiment cube” as a novel sentiment analysis tool that can provide
unlimited analysis viewpoints as the concept of unlimited cross operations of online
analytical processing (OLAP), (refer to 3.3: Step 2 – Extract and validation). After that,
we will get the customer sentiment information such as sentiment score, sentiment
ranking level, etc. in each analysis aspects, (refer to 3.3: Step 3 – Utilize and analysis
results).
Next is the knowledge sharing process between outside-in and inside-out
information. For the inside-out approach, we do the knowledge sharing process using
the survey by a questionnaire and interview in the data analysis step. So the datacollection consists of aspects from specialists, opinion of customers/non-customers inThai life insurance service. We used the sentiment analysis results which we got fromoutside-in information (critiques from social media) to be questions in the questionnaire
for the knowledge co-creation process. We utilize customer dissatisfaction issues which
we got from previous steps, to be the highlighted issues for proving in real practice andfinding out the appropriate problems solving methods in a questionnaire. They consist ofservice evaluation issues, claim issues, policy cancellation issues, and othermisunderstanding issues, (refer to 3.3: Step 4 – Sharing). To capture knowledge sharing
between customer sentiment and respondents who replied to the questionnaire, the resultsof customer sentiment knowledge sharing got from their experience, (refer to 3.3: Step 5– Capture and learning). Knowledge co-creation occurs in this step, the expected results
are feedback and idea for problem-solving methods from customers, non-customers andexpertise in life insurance area, (refer to 3.3: Step 6 – Create and leverage). In the final
step is implementation, we can find more knowledge after implement in the real practicethen we learn from mistake and improve it. In this step, the efficiency of monitoringprocess plays important role to catch up the riddle and require the process improvement,(refer to 3.3: Step 7 – Implementation, Step 8 – Learning and monitoring).

3.3 Customer Sentiment Knowledge Management Process37Figure 5: Customer sentiment knowledge management process
Step 1. Identify and AccessIn a part of store and access, it is necessary to identify a related knowledge suchas a source of sentiment. In the concept of social CRM, we have attention to findcustomer satisfaction using customer sentiment from social media using technology.
Then this task has developed a tool named “sentiment extraction tool”. However, develop
this tool requires business understanding. In this step (Step 1) has two tasks. There is
Task A: Life insurance business understanding with social CRM and Task B: Sentiment
Extracted Tool - Sansarn Tagging Tool as following.
1.1 Life Insurance Business Understanding with Social CRM
Life insurance process is the one of a process which closely the relationship between
the service process of company and customer who has left comments related to service onsocial web-blogging. To identify the life insurance service process, we have to review theservice process on this business in the principle of general practice. We explained interms of ‘Customer life cycle’ concerns on the management information, which consistsof three stages: customer acquisition, customer engagement, and customer retention. We
select only the processes which highly involve customers’ sentiment on social media.
Stage1: Customer Acquisition (Pre-purchase  purchase process)A service on customer life cycle starts from the company find out a list of prospect(expected customer) to offer new plan (product) or contract. Service touchpoint, acompany representative, introduces the customer's proper plan to meet customers'
requirements and their lifestyles, such as financial status, health condition, premium
payment, or sum assured earning expectation. Service touchpoint and prospect shouldmake understand and agreement on exceptions and conditions under a life insurance
contract. After the prospect decided to apply for a new life insurance contract, a new
application will be submitted to the underwriting department to verify customer
38

qualification and then issue a new policy contract and deliver to the customer by service
touchpoint. The acquisition stage will be a success by providing product and service
prominently with innovation to attract customers' minds with freewill service [45].
Stage2: Customer Engagement (Purchase  service support process)
After new policy contract was approved, customer status is changed from prospect to be
insured of a company due to, the customer will obtain service under contract conditions
and life insurance law such as service on claim assessment as life insurance proceeds.
Insured acquired an excellent service from service touchpoint when new procedures
occurred. For example, service touchpoint should be easy to contact a customer, help tosolve problems related to insurance service including general support in operation service
such as change policy status (make extended term insurance (ETI), reduced paid-up
insurance (RPU) or cancel policy), make loan from policy when cash value of policy
occurred.
(Note: insured is a policy owner or person whom the insurer (Life insurance Company) promises to pay adesignated beneficiary a sum of money (the benefit) in exchange for a premium, upon the death of an
insured person under policy contract conditions) [50].
Note: insured is a policy owner or person whom the insurer (Life insurance Company) promises to pay adesignated beneficiary a sum of money (the benefit) in exchange for a premium, upon the death of an
insured person under policy contract conditions) [50].

Once the insured requires to make a claim such major claim (death claim) or a minor
claim (illness claim), the customer has to contact the insurer to request compensation as alife insurance contract. The company department of service touchpoint should provide
professional service to the insurer by considering that claim, give advice, and decide
whether the insured’s policy cover the hospital costs as soon as possible. To provide
experienced service and follow-up with care to the insured makes the engagement
process better and increases customer loyalty [45, 83].
Stage3: Customer Retention (Re-purchase process)Customer Retention main point is to keep insured with active status or recall excustomer by resale with a new product or retain customer before leaving the company.
For example, when policy status changed to be mature (end of the contract), the insured
became inactive. However, retaining customers’ needs to get satisfaction from a customer
so listening to customer’s voices to find out dissatisfaction points on the current businessprocess is important.

39

Figure 6: Life insurance process across to CRM
This demonstrated scenario is a life insurance process across to CRM function,
consisting of People, Process, and Technology. An analysis of the CRM strategy consistsof customer acquisition, customer intention, and customer termination. Life Insurance
processes under service unit are service touchpoint, operation department including theprocess of new policy approval, policy change request, and claim assessment. All of theunits are analyzed by various systems such as CRM system, DWH/OLAP, BI, Sentiment
analysis with social media as Table 3 and Figure 6.
Three CRM strategies are concerned under customer life cycle of CRM
conceptualize related to life insurance business process: [Table 3]
1. Customer acquisition Customer acquisition is a strategy to extend a business's
market share by approaching new customers and making ex-customer returns. Usingsocial media channels, it has many ways to manage, such as launch a new campaign to a
direct-market group. It is also essential to understand the current situation of customer
sentiment and current problems in the acquisition stage for all of them. To assign a proper
aspect, it needs to study the business process. The process in life insurance of theacquisition stage as below: [Table 3 on item 1.1 – 1.5]
1.1: Start  service touchpoint (external such as agent or agency, internal such as callcenter: Refer to table.1) offer plan which appropriate to requirement  provide
important information & special exception  consider health condition & financial
performance  customer [Table 3 on item 1.1, 1.2, 1.3]40

1.2: Customer  reveal fact (personal information, health condition)  makeunderstand on new plan information with exceptional conditions  sent a new policy
contract to approval process  service touchpoint & operation department [Table 3
on item 1.4, 1.5]
Table 3: Life Insurance process related to CRM components and strategies
Remark: means this life insurance process has relationship with CRM component,  means this life insurance
process occasionally has relationship with CRM component, - means this life insurance process does not have any
relationship with CRM component.

People
CRM
Comp.

Life Insurance Process

Serv. TP.
EX

IN

OP
Dept.

Customer
Retention

CustomerAcquisition

CUST

Customer
Termination

Process

Technology
Claim Ass.

New
P.A.

P.C.R.
Minor

Major

CRM

DW

BI

SA

SMA

1.1 Propose appropriate plan as
customers' requirement







-



-

-

-





 



1.2 Explain clearly an important
information & special exception







-



-

-

-





 



1.3 Consider and reveal current
health condition & financial
performance







-



-

-

-





 



1.4 Make understand on new plan
information with exceptional
conditions







-



-

-

-





 



1.5 Submit a new policy contract toapproval process











-

-

-





 



2.1 Provide customer support whenpolicy 's requirement is changed
2.2 Support customer of claim
assessment with 3Cs verification
2.3 Follow up problems andcustomers' sentiment









-



-

-





 











-

-









 











-











 



3.1 Sue on court, make petition for
OIC , make an accusation toagent
3.2 Make policy cancellation andmake policy reject









-











 











-











 



3.4 Feedback in complaint system
and/or express sentiment onsocial









-











 



Table 3. Three basic components in customer relationship management process, where CUST="Customer", Serv.TP = "Service
Touch Point", EX = "External", IN = "Internal", OP Dept. = "Operation Department", New P.A. = "New Policy Approval", P.C.R.
= "Policy Change Request", Claim Ass= "Claim Assessment", CRM = "Customer Relationship Management", DW = "Data
Warehouse/OLAP", BI = "Business Intelligence", SA = "Sentimental Analysis", and SM = "Social Media Analysis"

From Table 3, there are sign strongly related to People-Service touchpoint &customers on the acquisition process. Hence, an analysis viewpoint requires someindicators to monitor service touchpoint performance. This analysis task provided a“service evaluation factor” of service touchpoint for evaluating their performance. The
service evaluation factors consist of reliability, consistency, dependability,
responsiveness, competency, access, courtesy, communication, knowledge, credibility,
security, and understanding. They are one of the main aspects of sentiment analysis in ourdesign.

41

Aspects identified in the acquisition stage are ‘Service touchpoint’ and ‘Service
evaluation factor,’ including sub-aspects identified under their aspects. [Table 3 on item
1.1 – 1.5, Table 4 on item 3&4]
The service touchpoint aspect has two sub-aspects. [Table 4 on item 3]- Internal service sub-aspect [Table 4 on item 3.1] has three sub-aspects. [Table 4 on item
3.1.1-3.1.3]- External service sub-aspect [Table 4 on item 3.2] has five sub-aspects. [Table 4 on item
4, 4.1-4.5]
The service evaluation factor aspect has five sub-aspects. [Table 4 on item 4, 4.1-4.5]
2. Customer intention
Customer intention is one strategy to keep a current customer by understanding
customers’ minds and solving misunderstandings between customer and business. Usingsocial media, finding dissatisfaction of company service to help to solve the problems isneeded. The process in life insurance of intention stage as below:
When a new policy is approved, customer status is changed to be ‘insured,’ then policy
protections and services are started under coverage. [Table 3 on item 2.1 – 2.3]
2.1: Customer  require support from service touchpoints (make “policy change
request” or make “claim assessment” process)  contact and request for service Service touchpoint [Table 3 on item 2.1, 2.3]
2.2: Service touchpoint & operation department  support follow standard
procedure  claim assessment verify 3Cs  return or reject payment to customer ascoverage rules  Customer [Table 3 on item 2.2, 2.3]
Policy change request will occur when customer’s requirement is changed, forexample, require new rider plan, request to change policy status such as makeextended term insurance (ETI), reduced paid-up insurance (RPU) or cancel a policy,
etc. [Table 3 on item 2.1, 2.3]
Note: ETI is a clause under many policies that gives the option of continuing the existing insurance for a period basedon the contract's cash value. RPU is a policy whose cash value can be used to buy paid-up insurance in the highest
amount it can afford. Source: http://www.businessdictionary.com/definition- The claim assessment process is one of the important processes in life insurance,
which is the process to request the insurer to pay to return coverage of loss under thepolicy's terms. Payment consideration is under "Claim Assessment Triangle Theory(3Cs)," which consists of three elements, cause of the claim, coverage terms, andcontractual legality [44]. [Table 3 on item 2.2, 2.3]
Aspects identify on intention stage is ‘Process and Operation’ including subaspects also identify under their aspects. [Table 3 on item 2.1– 2.3, Table 4 on item 5]- Process and Operation aspect has three sub-aspects. [Table 4 on item 5]- Policy Operation sub-aspect [Table 4 on item 5.1]
- Claim Operation (type) sub-aspect [Table 4 on item 5.2] has two sub-aspects.
[Table 4 on item 5.2.1-5.2.2]- Claim Issue (Assessment) sub-aspect [Table 4 on item 5.3] consists of ‘Advancepayment’, ‘Reimbursement denial’, ‘Concealment of medical records’, ‘Delayed service

42response’, ‘Fax claim issues’, ‘Misapprehension’ sub-aspect [Table 4 on item 5.3.1,
5.3.6] .
Aspects identify on termination stage are ‘Impact level’ (Dissatisfaction impact type)including sub-aspects that are identified under their aspects. [Table 3 on item 3.1 – 3.3,
Table 4 on item 6]
Dissatisfaction impact type aspect has three sub-aspects. [Table 4 on item 6]- High impact type sub-aspect [Table 4 on item 6.1] has three sub-aspects. [Table 4
on item 6.1.1-6.1.3]- Medium impact type sub-aspect [Table 4 on item 6.2] has six sub-aspects. [Table 4
on item 6.2.1-6.2.6]- Low impact type sub-aspect [Table 4 on item 6.3] has one sub-aspect. [Table 4 onitem 6.3.1, 6.3.2]
Aspects identify on all stage are ‘Social customer relationship management’ [Table 4 onitem 7’ consists of ‘Customer acquisition’, ‘Customer retention’, ‘Customer termination’,‘Public relation’ [Table 4 on item 7.1-7.4].
3. Customer termination
Customer termination includes cancellation, reject and termination action, occurs
from some dissatisfaction took place or the period of contract reach to the maturity date.
Exploring customers’ dissatisfaction is one way to reduce a termination rate by find out
misunderstanding issues from social media or make a questionnaire or implement a
conservation system. [Table 3 on item 3.1 – 3.3]
Impact levels (Dissatisfaction types) that motivate customer termination are classified
into three types.
3.1 High impact type such as customer make sue on the court, make a petition for OIC(Office of Insurance Commission is the organization which has a duty to control andpromote Thai Insurance industry) or accuse agent. [Table 3 on item 3.1, Table 4 on item
6.1]
3.2 Medium impact type, such as make a policy cancellation or policy rejects. [Table 3 onitem 3.2, Table 4 on item 6.2]
3.3 Low impact type includes giving negative feedback to the complaint system directly
like a questionnaire or expressing a negative feeling of their experience on social media.
[Table 3 on item 3.3, Table 4 on item 6.3]
Aspects identify on termination stage are ‘Impact level’ (Dissatisfaction impact type’)
including sub-aspects that are identified under their aspects. [Table 3 on item 3.1 – 3.3]
Aspects identify on termination stage are ‘Impact level’ including sub-aspects that are
identified under their aspects. [Table 3 on item 3.1 – 3.3, Table 4 on item 6].
Dissatisfaction impact type aspect has three sub-aspects. [Table 4 on item 6]- High impact type sub-aspect [Table 4 on item 6.1] has three sub-aspects. [Table 4
on item 6.1.1-6.1.3]- Medium impact type sub-aspect [Table 4 on item 6.2] has six sub-aspects. [Table 4
on item 6.2.1-6.2.6]- Low impact type sub-aspect [Table 4 on item 6.3] has one sub-aspect. [Table 4 onitem 6.3.1]
43

1.2 Sentiment Extracted Tool - "Sansarn Tagging Tool"
This research, we used “Sansarn Tagging tools” is a tool for helping extract seed ofwords (keywords) which has designed and implemented for support this task using PHP
programming. This tool provides the modules to pull out words that are under ourdesigned aspects and sub-aspects [59]. We can extract three kinds of clue term (clue term)
as our identified aspects/sub-aspects related to life insurance domain in Thai language.
The process starts from the crawling process to gather the user-generated contentsfrom the criticized web-blogs named pantip.com. This website is one of the famous
critiqued websites in Thailand, which provides free blogs for users who participate in theThai life insurance domain. They might be customers, insures, ex-customers, orprospects. People who have experience in life insurance express their sentiment via thiswebsite. This corpus from web-blogs consists of linguistics terms and non-linguistic
terms such as Thai synonymous words, transliterated words, abbreviations, and slang. So
the process of text preprocessing such sentence segmentation on corpus is required. The
tagged corpus was prepared from random discussion topics and was annotated into each
aspect and sub-aspects.
The main tagging modules are classified into five main modules which consist of‘Intention’, ‘Engagement Stage’, ‘Product (Topic)’, ‘Regulation (Topic)’, Service Center(Topic)’.
1. Intention means this tagging sentence shows the intention or purpose objective. It
has five types of analysis that compose ofa. Intention type means this tagging sentence shows requirement.
b. Question type means this tagging sentence shows any suspicion by asking
some question.
c. Admiration type means this tagging sentence shows a positive feeling of
the owner of the sentence.
d. Complaint type means this tagging sentence shows a negative feeling of
the owner of the sentence.
e. Informative type means this tagging sentence gives some related
information.
f. Cause type this tagging sentence gives the reason for information.
g. Other types.
Sentence (Chunk) “รอเคลมนานมาก ประมาณ 4 ชัว่ โมงได้ ได้ออกประมาณ 4 โมงเย็นแล้วต้องขนของหนีน้าด้วย เลยจ่าย
เองไปเลย / wait claim process too long time, wait around 4 hrs. go out at 16.00 pm andneeds to take my belonging from flooding so we needs to paid by ourselves”- Clue word (Complaint-COM): “รอเคลมนานมาก/wait claim process too long” and “เลยจ่ายเองไปเลย/so paid by ourselves”- Clue word (Informative-INF): “ประมาณ 4 ชัว่ โมงได้ ได้ออกประมาณ 4 โมงเย็น/wait around 4
hrs. go out at 16.00 pm.)”

44

Figure 7: Sansarn tagging tools on Intention screen Example 1
Sentence (Chunk) “ที่บอกว่าประกันไม่ดี ประกันโกง นัน่ นี่ มันมาจากตัวแทนที่เลวร้ ายประพฤติตวั ไร้ จรรยาบรรณไม่กี่คน
เท่าที่ผ่านมาก็เคลมได้ ไม่มีปัญหา/ that’s said, insurance is not good. Insurance cheat. It comes froma bad agent, behaved unethically, few people. In the past, claim process is no problem.”- Clue word (Admiration-ADM): ‘เท่าที่ผ่านมาก็เคลมได้ / In the past, claim process is no
problem’ and ‘ไม่มีปัญหา/no problem’- Clue word (Complaint-COM): ‘ประกันไม่ดี/insurance is not good’, ‘ประกันโกง/Insurance cheat’, ‘จากตัวแทนที่เลวร้ าย/ a bad agent’ and ‘ประพฤติตวั ไร้ จรรยาบรรณ/ behaved
unethically’Figure 8: Sansarn tagging tools on Intention screen Example 245

2. Engagement Stage uses for specific the customer stage on customer life cycle
such as prospect, customer or ex-customer, etc.
a. Prospect stage
b. Insured/Customer stagec. Ex-Insured/Ex-Customer staged. OtherSentence (Chunk) “ผมซื ้ออยู่ ไม่วา่ จะ AIA เมืองไทย ไทยประกัน อยุธยาอลิอนั ซ์ ถ้ าบริษัทไหนบอกว่าจ่ายเบี ้ยเท่ากันไป
ตลอด บอกได้ 100% ว่าหลอกลวงครับ/ I bought, even if AIA, MuangThai, or Ayudhya Allianz thecompany says that pay the same amount to say 100% that is scam..”- Clue word (Engagement Stage-INS): ‘ผมซื ้ออยู/่ I bought’Figure 9: Sansarn tagging tools on Engagement screen
3. Product means this tagging sentence shows the concerned product. It has six
groups of product that composes ofa. Basic-Endowment
b. Basic-Whole Lifec. Basic-Termd. Basic-Universal Life
e. Basic-Single License
f. Riderg. OtherSentence (Chunk) “ส่วนเรื่องค่าเบี ้ยประกันสุขภาพ มีการปรับตามช่วงอายุทกุ บริษัทครับ / Health insurance
premium have to adjust to all ages range.”- Clue word (Engagement Stage-INS): ‘ผมซื ้ออยู/่ I bought’
4. Regulation means this tagging sentence shows the concerned operation andregulation. It has seven operation type ,which composes ofa. Marketing Campaignb. Underwriting Rulesc. Actuarial Rules
d. Claim Processe. Policy Changing Rules
f. Policy Operationg. Lawh. Other46

Figure 10: Sansarn tagging tools on Product screen

Figure 11: Sansarn tagging tools on Process screen 1
Sentence (Chunk) “ถ้ าไม่แถลงไป ถ้ าเขาสืบได้ วา่ เราโกหก เขาจะไม่จา่ ยค่ารักษาครับ มีสิทธิยกเลิกประกันได้ ครับ และมีสิทธิ
เรียกร้ องค่าสินไหมเขาเคยจ่ายไปแล้ ว คืนมาได้ ทงหมดครั
ั้บ/ If concealing, if company know we are lying.
The company can reject payment for treatment. Have the right to cancel the policy. And
the company can request to return all of the expenses which the company paid for us.
Return all.”- Clue word (Regulation (Topic)-CLM): ‘ไม่แถลงไป / conceal,’ ‘สืบได้ วา่ เราโกหก/ company
know we are a lie,’ ‘ไม่จา่ ยค่ารักษา/ reject payment for treatment,’’มีสิทธิ์เรี ยกร้ องค่าสินไหมเขา
เคยจ่ายไปแล้ ว คืนมาได้ ทงหมด/ั้
company can request to return all of the expense which
company paid.’- Clue word (Regulation (Topic)-PCR): ‘มีสิทธิยกเลิกประกัน/ Have the right to cancel
policy’5. Service Center means this tagging sentence shows the concerned service
touchpoint who handle this case. It has four service touchpoint types thatcomposes ofa. Agent and Agency
b. Call Center47

c. Customer Service Centerd. Training Centere. OtherSentence (Chunk) “ขึ ้นกับตัวแทนจริงๆค่ะ เอ ไอ เอ ก็ดีนะคะ น้ องสาวเราทาอยูเ่ คลมได้ ดี และไม่มีเพิ่มเบี ้ยค่ะ/, dependon agent, AIA is also good, my sister request claim assessment and process are good andno extra charge premium.”- Clue word (Service Center (Topic)-AGT): ‘ตัวแทน/agent.’- Clue word (Service Center (Topic)-SOT): ‘เอ ไอ เอ/AIA.’

Figure 12: Sansarn tagging tools on Process screen 2
Developing programming and tools:
For data preprocessing, we use PHP (PHP Hypertext Preprocessor) and iMacro on
Firefox to crawl data from websites and directly input it into Navicat for MySQL
database version 8.0.8.
For text preprocessing, natural language processing use JAVA script and Python, andsentiment classification task use Javascript 1.7.
Tagging tools, SANSARN develop on PHP. For multidimensional sentiment cube
generation, we develop on Microsoft SQL Server connect through Cognos Framework
Manager 10.2, Cognos Transformer 10.2, Cognos Report builder 10.2.
Step 2. Extract and Validation
This is to define the knowledge structure, categorize the categories to search, restore, anduse easily, improve, modify, or create some knowledge to be suitable for usage andanalysis. This section presents our design of a multidimensional sentiment cube, and its
dimension structure, which was created from our previous information extraction called
sentiment extraction tool. Natural language processing, online analytical process (OLAP)
and sentiment analysis concept in the aspect-based analysis was utilized in this task.
Design of Multidimensional Sentiment Cube
This section presents our design of a multidimensional sentiment cube and its
dimension structure, followed by information extraction of sentiment keywords (Lexicon)
and aspect-based sentiment analysis.
48

2.1. Design of Multidimensional Sentiment Cube and its Dimension Structure
A definition of a sentiment cube system as the previous section illustrates our
proposed design of a multidimensional sentiment cube for the Thai life insurance
business. It deployed the social CRM concept and its practice use with three main
properties, i.e., multi-dimensions, hierarchical dimensions, and multi-measurements.
A sentiment cube system is proposed to store sentiment elements inside, together
with their target aspects. Its data structure consists of a set of cells, organized in the formof dimension structure. The system keeps sentiment values associated with a set of entity
features (aspects) and then populates a hierarchy of the cells in the sentiment cube withthe sentiment values. An analysis is a process that aims to determine the attitude of a
speaker concerning some topic. A dimensional scheme is a conceptual grouping ofdimensions. It is a generalization of a constellation. Dimensions are grouped aroundnodes that model the dimensions that may be used together in the same analysis.
The automatic of sentiment analysis is the training process to identify sentiment
within the contents. This multidimensional model is a conceptual model adapted tosentiment analysis from unstructured data. This model targets to provide the analyst withan adapted Online-Analytical Process (OLAP) conceptual view to manipulate the model'sconcepts using OLAP operations. The hierarchy of each dimension, including sentimentvalues associated with a set of entity features, and then populates a hierarchy of the cells
in the sentiment cube with the sentiment values. Sentiment analysis modules affect a setof operations on the sentiment cube.
The cube generation needs to digest the contents from Thai life insurance critique
blogs from narratives into the cube cell corresponding to each aspect (dimension –parent) and sub-aspect (sub-dimension – child) for analyzing the content across different
cells, which related to the target dimension and measurement. This task gathers the datafrom critiqued posts, which extract to be various sub-aspects are, consists of tree-likehierarchically structured data. It will be clearly shown for easily navigation through data
during analyses [84]. We derived the concept of the multidimensional cube, which isdefined based on document base and increases a sentiment scheme for more potential
explanations in a group feeling phenomenon on the definition as in the previous section[69].
In this research, we utilized the sentiment analysis concept to merge into a
multidimensional cube to be a multidimensional sentiment cube.
We demonstrated in detail on our previous task on Thai Life Insurance business-related
under social CRM concept.
Definition: Multidimensional Sentiment Cube
The formal description is as follows.
Let SC = (D, H, S, F, M) be a multidimensional sentiment cube where D = {D1, D2, . . . ,Dn} is a set of aspect and sentiment dimensions, H = {H1, H2, . . . , Hn} be the set ofhierarchical structures. Each hierarchical structure Hi is a tree (a single-rooted directed
acyclic graph), composed of a set of nodes (or vertices), and edges (or paths), Hi = (Ni,Ei), where Ni is a set of nodes {ni0 , ni1 , ni2 , . . . , nipi } and Ei is a set of directed edges{ei0 , ei1 , ei2 , . . . , eiqi }, of the i-th dimension Di.
49

Here, the apex (ni0 ) of Hi represents the whole and nij Ni is an j-th instance node in thetree Hi. The sentiment set S = {+, −, 0} expresses positive, negative and neutral
sentiments. Each leaf node nl is given a sentiment, denoted by S(nl). The footprint set F ={(h1k1 , h2k2 , . . . , hnkn )} is the set of all possible combinatorial footprints (k1, k2, . . . , kn)of H. The footprint-sentiment mapping M F × S → R is the sentiment model,
representing mapping of the footprints and sentiment types onto a real number (amembership value of the footprint in the specified sentiment type).

Figure 13: An example of a multidimensional sentiment cube for Thai life insurance business, where the x-axis
expresses the insurance process and operation, the y-axis denotes the insurance product, and the z-axis represents
the service touchpoint. When texts are streaming into the system, a sentiment (–, +, or 0) is extracted for eachportion (sentence) in the texts, forming a stream of sentiment. The streaming sentiments are counted and kept intheir corresponding cells.

Fig.13 shows an example of a sentiment cube, particularly designed for Thai life
insurance business. The cube forms a multidimensional hierarchical platform wherein thecells are arranged in a structure of N-dimensional data, in this figure N = 3. Three
dimensions define three entity aspects, i.e., ‘insurance product (IP)’, ‘service touch-point(ST)’, and ‘insurance process and operation (IO)’. That is, D = {IP, ST, IO}. Moreconcretely, the x-axis shows insurance process and operation, y-axis is an insurance
product, and z-axis represents service touchpoint. Each cell stores a sentiment streaming
value of negative, neutral, or positive (-, 0, or +).
Furthermore, an entity aspect can hold a set of sub-aspects (sub-dimensions), forming
metadata that define dimensions and their associated sub-dimensions in a repeatedmanner. For example, the ‘insurance product’ has ‘life’ and ‘rider’ as its sub-aspect, andat the bottom layer, a sentiment value is stored. In the figure, the cell labeled with A isthe sentiment of ‘conceal (CON)’ under claim issue of ‘endowment product (EN)’ when
the service touchpoint is ‘agent (AGT).’ A sentiment storage module manages sentimentvalues associated with entity aspects (or features). Towards the construction of asentiment cube, one needs to digest and summarize the contents in Thai life insurance
critique blogs, often narratives, into the structure of a data cube. This sentiment value is50derived from the summation of sentiment scores obtained from all textual fragments that
relate to the focused topic, in this case, CON-EN-AGT. After the cube construction, we
can use a well-defined set of online analytical processing (OLAP) operations to explore
the complicated facts in the multidimensional and the hierarchical point of view.
With this OLAP concept, one can get an insight via navigation through data with
exploratory analysis. A cell in a data cube typically corresponds to an aspect (dimension
or parent) or a drill-down sub-aspect (sub-dimension or child) for analyzing the content
across different cells related to the target dimension and measurement.
An example of insurance-related sentiment cubes

Figure 14: An example of a multidimensional sentiment cube for Thai life insurance business,where x-axis is issue characteristics, y-axis is product/service type, and z-axis is service touchpoint. Eachcell stores a sentiment streaming value of -, 0, or +Refer to Figure 14, assume that an insurance company designs a sentiment cube SC
comprised of three dimensions. In this sentimental cube, three dimensions are expressed
under three sentimental polarities; positive (+), negative (–), and neutral (0). In the figure,the x axis expresses issue type, the y axis denotes product/service type, and the z axis
represents service touchpoint type.
𝐷 = {𝐼𝑂𝑃, 𝑆𝑇𝑃, 𝐼𝑆𝑃} (IOP = insurance operation, STP = service touchpoint, ISP =insurance product).
𝐻1 = {𝐼𝑂𝑃, {𝑂𝐶𝐴, 𝑃𝐶𝑅}, {(𝐶𝑇𝑃, 𝐶𝑃𝐵, 𝐶𝑀𝑆), (𝐸𝑇𝐼, 𝑅𝑃𝑈)},𝐻2 = {𝑆𝑇𝑃, {𝐼𝑁𝑇, 𝐸𝑋𝑇}, {(𝐴𝐺𝑌, 𝐴𝐺𝑃), (𝐵𝐴𝑁, 𝐶𝐶𝑇, 𝐶𝑆𝐶)}}, and𝐻3 = {𝐼𝑆𝑃, {𝐿𝐹𝐸, 𝑅𝐷𝑅}, {(𝑊/𝐿, 𝐸/𝑁, 𝑇𝑅𝑀), (𝐻&𝑆, 𝐸𝐶𝑅)}},F = {(OCA, INT, AGT), (OCA, EXT, AGY), (OCA, BAN, CCT), (CTP, AGP, AGT), ...} andM = {(OCA, INT, AGT, +):2.5, (OCA, EXT, AGY, ):4.5 , (OCA, BAN, CCT, 0):6, (CTP, AGP,
AGT, +):0.2, ...}.

51

Our task, generating the cube, needs to digest the contents from Thai life insurance
critique blogs from narratives into the cube cell corresponding to each aspect (dimension– parent) and sub-aspect (sub-dimension – child) for analyzing the contents acrossdifferent cells related to the target dimension and measurement. The example illustrations
have to provide the following functions in four steps.
First, to identify the aspect (dimension) and sub-aspect (sub-dimension) in ahierarchical structure. The second is to make a process of data preparation, text mining
process with lexicon preparation, and mapping unstructured data corresponding with thedesignate as aspect (dimension) and sub-aspect (sub-dimension) related to measurements
determination. The third is to design three cube model generations, and the last is to makean efficient materialization.
However, our research utilizes a multidimensional cube with sentiment analysis
called ‘multidimensional sentiment cube.’ The measurements consist of the number ofwords or phrases matching with the lexicon and their sentiment score in each dimension.
2.2 Dimension structure design on aspect identification from business understanding
The design of a multidimensional sentiment cube for the Thai life insurance
business using social CRM and its practice starts from declared three main properties(multi-dimensions, hierarchical dimensions, and multi-measurements).
Multi-dimensions are gathered from the individual dimension, assigned from each aspect
under covered by analysis criteria. To identify aspects and sub-aspect under the form of
hierarchical structure, which are the main elements in cube generation, requires abusiness understanding of its principle. Hierarchical is a many-to-one relationship on
specific levels, representing a relationship among different sub-aspects under the same
aspects within a hierarchy [35]. Therefore hierarchical dimensions are represented by allof the sub-aspects in different layers under the same aspect relationship. When designing
the group of aspects to be multi-dimensions, it needs to concern with hierarchicaldimensions at the same time.
For easy understanding, we draw a process flow on business process mining
notation (BPMN) for process reference as below.
A line area shows the process of approaching new customers and submits newpolicy contract. It indicates that the processes which effect on acquisition stage are: theprocess of product (plan) offering to the customer, the process of policy approval, andissue of a new policy.
B line area shows the process to announce a result of policy approval. The
process of policy approval so this process checking step is checking (prepare new policy
contract / inform result accept/reject to a customer).
C line area shows the process to make a request on the claim assessment process.
Customers require after-sales service.
D line area shows the claim verification process. In general, insured expect to getcompensations cover all of their payment.

52

B

A

Figure 15: Customer Acquisition Stage in Life Insurance with line A and B

C
C.1

D
C.2

D.1

Figure 16: Customer Engagement & Retention Stage in Life Insurance

53

D.2

Aspects and Sub-aspects Identification
Our task introduces two kinds of aspect which have to concern dimension structure
design.
a. Ownership representative aspect is the owner of subject or object which was
expressed sentiment or mentioned by opinion reviewer under analysis purpose ofmultidimensional sentiment cube.
b. Analysis viewpoint aspect is subject or objects which were expressed sentiment or
mentioned by opinion reviewer under analysis purpose of multidimensional sentiment
cube.
Ownership representative aspect:
The purpose of our multidimensional sentiment cube is under a business viewpoint,
so it is indispensable to state about company aspect and product aspect (plan in life
insurance).
A ‘Company’ aspect of identification on representative ownership aspect is the Thai
life insurance company name. We gathered user-generated contents from various
companies such as American International Assurance (AIA), Thai Life Insurance, Muang
Thai Life Assurance, Allianz Ayudhya, Bangkok Life Assurance, Thai Samsung Life
Insurance, SCBLIFE Assurance, including with some health insurance such as BUPA
health insurance, SIGNA health insurance, etc. [Table 4 on item 1]‘Product aspect identification on representative ownership aspect is ‘Life’ (Basicplan) and ‘Rider’ (Supplemental plan). Life is the main product consists of Endowment,
Whole Life, Investment, Retirement and Term [Table 4 on item 2.1.1-2.1.5] and Rider is
supplement product composes of HS, HB, AI, AI/RCC, ECIR, CR, disability and WP.
[Table 4 on item 2.2.1-2.2.8]
Analysis viewpoint aspect:
Analysis viewpoint aspect identification starts from analyzing the relationship among
business functions which impact on customer sentiment. This step explains the relation oflife insurance functions to the CRM concept of service process that their negative or
positive sentiment can use to improve customer satisfaction. After finding out theirrelationship among main aspects, the system eventually presents the illustration of main
properties; multi-dimensions, hierarchical dimensions, and multi-measurements.
Aspects identify on acquisition stage are ‘Service touchpoint’ and ‘Service evaluation
factor’ including sub-aspects that are identified under their aspects. [Table 3 on item 1.1 –
1.5, Table 4 on item 3&4]
Service touchpoint aspect has two sub-aspects. [Table 4 on item 3]- Internal service sub-aspect [Table 4 on item 3.1] has three sub-aspects. [Table 4 onitem 3.1.1-3.1.3]- External service sub-aspect [Table 4 on item 3.2] has five sub-aspects. [Table 4 onitem 4, 4.1-4.5]
Service evaluation factor aspect has five sub-aspects. [Table 4 on item 4, 4.1-4.5]
Aspects identify on intention stage is ‘Process and Operation’ including sub-aspects also
identify under their aspects. [Table 3 on item 2.1 – 2.3, Table 4 on item 5]54Process and Operation aspect has three sub-aspects. [Table 4 on item 5]- Policy Operation sub-aspect [Table 4 on item 5.1]
- Claim Operation (type) sub-aspect [Table 4 on item 5.2] has two sub-aspects. [Table
4 on item 5.2.1-5.2.2]- Claim Issue (Assessment) sub-aspect [Table 4 on item 5.3] consists of ‘Advancepayment’, ‘Reimbursement denial’, ‘Concealment of medical records’, ‘Delayed service
response’, ‘Fax claim issues’, ‘Misapprehension’ sub-aspect [Table 4 on item 5.3.1,
5.3.6] .
Aspects identify on termination stage are ‘Impact level’ (Dissatisfaction impact type)including sub-aspects that are identified under their aspects. [Table 3 on item 3.1 – 3.3,
Table 4 on item 6]
Dissatisfaction impact type aspect has three sub-aspects. [Table 4 on item 6]- High impact type sub-aspect [Table 4 on item 6.1] has three sub-aspects. [Table 4
on item 6.1.1-6.1.3]- Medium impact type sub-aspect [Table 4 on item 6.2] has six sub-aspects. [Table 4
on item 6.2.1-6.2.6]- Low impact type sub-aspect [Table 4 on item 6.3] has one sub-aspect. [Table 4 onitem 6.3.1, 6.3.2]
Aspects identify on all stage are ‘Social customer relationship management’ [Table 4 onitem 7’ consists of ‘Customer acquisition’, ‘Customer retention’, ‘Customer termination’,‘Public relation’ [Table 4 on item 7.1-7.4].
The identification of Aspect & Sub-aspect summarization: (Multi-Dimensions
Determining)No.
(Dimension/Sub-dimension)
0. Transaction ID
1. Company X
1.1 Company X
1.2 Company Y
1.3 Others (No information)
2. Product2.1 Life (Basic)
2.1.1 Endowment
2.1.2 Whole life
2.1.3 Investment
2.1.4 Retirement
2.1.5 Term
2.2 Rider (Supplement)
2.2.1 Hospital & Surgical expenses (H&S)
2.2.2 Hospital benefit (HB)
2.2.3 Accident indemnity rider (AI)No.
(Dimension/Sub-dimension)
5. Process and operation
5.1 Policy operation
5.2 Claim operation (Claim type)5.2.1 Minor claim
5.2.2 Major claim
5.3 Claim Issue (Claim assessment)
5.3.1 Advance payment
5.3.2 Reimbursement denial
5.3.3 Concealment of medical records
5.3.4 Delayed service response
5.3.5 Fax claim issues
5.3.6 Misapprehension
5.3.6.1 Misapprehension in coverage
5.3.6.2 Misapprehension in protection
5.3.6.3 Misapprehension in period
5.3.6.4 Misapprehension in exception

2.2.4 AI/riot&civil commotion (AI/RCC)
2.2.5 Enhanced critical illness rider (ECIR)
2.2.6 Cancer death & income benefit (CR)
2.2.7 Disability
2.2.8 Waive premium (WP)
2.3 Aﬀinity marketing
2.4 Special product2.4.1 Social insurance

55

6.

Impact level
6.1 High impact
6.1.1 Lawsuit
6.1.2 Petition to off. of insurance com.(OIC)
6.1.3 Make an accusation to agent
6.2 Medium impact
6.2.1 Policy cancellation
6.2.2 Policy rejection

2.4.2 Thai 30-BHT health insurance
2.4 Others (No information)
3. Service touchpoint
3.1 Internal
3.1.1 Call center & customer service center
3.1.2 Operation dept.: claim, underwriting
3.1.3 Telemarketing
3.2 External
3.2.1 Agent or agency
3.2.2 Bancassurance
3.2.3 Credit card
3.3 Others
4. Service Characteristics (Service evaluation)
4.1 Reliability
4.2 Responsiveness4.3 Characteristics
4.4 Consistency
4.5 Knowledgeability
4.6 Others
8. Measurement
8.1 No. of sentences
8.2 No. of words on company
8.3 Total scores on company
8.4 No. of words on product8.5 Total scores on product8.6 No. of Words on service touchpoint
8.7 Total Scores on service touchpoint
8.8 No. of words on service characteristic
8.9 Total scores on service characteristic

6.2.3 Policy invalid
6.2.4 Policy void
6.2.5 Policy surrender
6.2.6 Loan
6.3 Low impact
6.3.1 Negative impression
6.3.2 Positive impression
7. Social Customer relation management
7.1 Customer acquisition
7.2 Customer retention
7.3 Customer termination
7.4 Public relations

8.10 No. of words on process & operation
8.11 Total scores on process & operation
8.12 No. of words on impact level
8.13 Total scores on impact level
8.14 No. of words on negative words
8.15 Total scores on negative words
8.16 No. of words on positive words
8.17 Total scores on positive words
8.18 Total scores on service characteristic

Table 4: List of dimensions, sub-dimensions and measurements of multidimensional
sentiment cube
From business knowledge understanding as above, we analyzed the relationshipamong business functions which impact on customer sentiment. Then we summarize themulti-aspects which can refer to be dimension of multidimensional sentiment cube astable 4. Multi-dimensions and hierarchy dimensions design depend on dimensions andsub-dimensions design. Dimension is aspect and sub-dimension is sub-aspect. Table 4shows main item is aspect (dimension) such as line no. 1, 2, 3, 4, 5, 6. Sub-aspect (subdimension) is the next column layer such as 1.1, 1.2, 2.1, 2.2, 3.1, 3.2, etc.
However, sub-aspect is possible occur the multi-layers. It depends on the analysis
requirement so next of next layer of table 4 such as 2.1.1, 2.1.2, 2.1.1 ... will refer to subaspect as well. This characteristic is similar to hierarchical structure. Therefore aspect(dimension) is parent level and sub-aspect is child level in the hierarchical structure ofmultidimensional sentiment cube likewise.
2.3 Methodology of Multi-dimensional sentiment cube56

A recommendation method using multidimensional sentiment mining by taking benefitof multidimensional sentiment cube and data mining method as following: (Figure 17)

Figure 17: Multidimensional sentiment cube mining methodology
1. Knowledge Base preparation: Study business knowledge and preparation therelationship of features in our case study. This process is possible to makeparticipation with domain expert. (Refer to Item 3.1) The hierarchical structures
based on business knowledge are as flow below.
2. Identify related features in hierarchical form:

Figure 18. Service Touchpoint Hierarchy

57

Figure 19. Service Process: Policy operation & Claim assessment Hierarchy

Figure 20. Product (Plan) Hierarchy

Figure 21. Impact Type Hierarchy

Figure 22. Related Aspects Hierarchy58

3. Corpus and Lexicon construction based on aspect-based sentiment analysis:
Aspect-Based sentiment analysis concept:
We deploy the concept of sentiment analysis for extracting sentiment and aspects
elements from unstructured data by using standard opinion are a quintuple; [24, 28].

(ai, saij, sijkl, rk, pl)
Where ai is an aspect name, saij is a sub-aspect of ai, sijkl is the sentiment on sub-aspectsaij of aspect ai, rk is the opinion reviewer, and pl is the period when the opinion isexpressed by rk. The sentiment sijkl is positive, negative, or neutral
This research require total sentiment of customer satisfaction, som

Total score of customers’ sentiment =

 (S

)

in , jn , kn , lnn 1Whereas Sin , jn, kn, ln  (ain, sain , jn, Sin , jn , kn , ln, rkn )Where ai is an aspect name, saij is a sub-aspect of ai, sijkl is the sentiment on sub-aspectsaij of aspect ai, rk is the opinion reviewer.
The sentiment sijkl is positive, negative and n is number of critiqued word chunk.
When ai = {prd, sp, is} is a set of aspects (parent dimension of a cube)
rk = {st, cm} is a set of opinion reviewer (parent dimension of a cube)
sain  (sa11ij, sal2ij)
sal1ij = {lfprd, rdprd, amst, spst, posp, sttst, svst, posp, casp, hiis, miis, siis } is a set of sub-aspects(child dimension of a cube level1)
sal2ij = {ctca, cpca} is a set of sub-aspects (child dimension of a cube level2)
sijkl = {negative, positive}
Aspect in parent level: st = service touchpoint, cm = company, prd = product(plan), sp =service process, is = impact & sentimentSub-aspect in child level 1:
lfprd = life (Basic) of prd, rdprd = rider of prd,
amprd = affinity marketing of prd, spprd = special product of prd,
sttst = service type of st, svst = service evaluation of st,
posp = service operation of sp, casp = claim assessment of sp
hiis = high impact type of is, miis = medium impact type of is,siis = low impact type of is
Sub-aspect in child level 2:59ctca = claim type of sp, cpca = claim problem of sp
4. Text preprocessing and Information extraction with Text mining and Natural
Language Processing (NLP) in Thai language.
This section has many tasks which need to process respectively below:- Create lexicon construction using aspect-based sentiment analysis concept
o Refer to Section 3.2, Aspect-based sentiment analysis concept withformula  Define : an aspect name as ai, a sub-aspect of ai as saij,
sentiment on sub-aspect saij of aspect ai as sijkl, rk is the opinion
reviewero Text preprocessing in Thai language with text mining and NLP- Corpus preparation – www.pantip.com- Lexicon construction as specific aspect/sub-aspects with“SANSARN tagging tool”- Text preprocessing process
o Define sentiment word scoring by SO-CAL concept- Exploiting linguistic knowledge for sentiment classification- Information extraction process from corpus and lexicon => convert unstructured
data to structure data
- Database management (ETL,…cleansing)- Data population in database – Fact table
The significance of lexicon construction for multidimensional sentiment cube on
unstructured data with text mining:
Text mining is the discovering process of new knowledge through the analysis of the
text. Information extraction task is one of the main tasks of the text mining process to dig
out the words or phrases that match the related lexicon from the main corpus. Information
extraction also analyzes unstructured text and identifies key phrases and relationships
within the text [18]. To analyze unstructured data to get hidden knowledge from all databring more advantages to a business. However, traditional cube dealing well withstructured data, so there are challenges for analyzing unstructured data into cube merge
with sentiment analysis. Unstructured data such as narrative text fields bury causal
factors which do not explicitly exist. It is important to flexibly support analysts in thatdata mining with OLAP and text content analysis (Unstructured data) in an integrative
manner [99]. However, OLAP can only support such integrative analysis. OLAP cansupport drill-down and roll-up feature on structured attributed dimensions. However, theycannot analyze support drill-down or roll-up on unstructured data such as critiques
sentences from people.
For this reason, understanding the analysis viewpoint requires knowledge tounderstand the cube relate to the contents of all the narratives corresponding with theirmeasurements. The text mining in this task has the main purpose of building a particular
lexicon that supports CRM and life insurance business knowledge.

60

The significant of NLP on text mining and aspect-based sentiment analysis:
In general, non-standard language, such as the Asian language, has uniquecharacteristics, especially, clarify the end of each sentence is difficult. The Thai language
also has a lot of ambiguous meaning and conversational language in sentences. The Thai
language's challenge problems are how to manage the long sentence and how to separate
wording with correct meaning [16, 61, 63, 77]. To extract sentiment from the Thai
language with a standard dictionary or shared corpus, the same as the English language,
is not easy. Especially, word of the mount from social media is a free-form format thatconsists of chat language, slang, or transliterated words, etc. It requires a text
preprocessing process with NLP to handle it.
To concern about cube usage, a hierarchical platform need to be recognized.
Lexicon characteristics should be declared words, phrases, or sentiment words for
extracting at the lowest level of a cell of sub-aspects, which is able to support drill-down
and roll-up of cube functions. Well-identified sentiment words of the lexicon contributeto the accuracy of sentiment analysis. To support the drill-down or roll-up of a cube, theaspect, and sub-aspect hierarchical structure is needed to define such a tree diagram. In
real-world applications, the polarities of many words depend on the aspects [45], and thedifferentiation of prior and contextual polarity is crucial [83]. For example, “มีเซอร์ วิสมายด์”under service means “(someone) has a service mind.” This word does typically not haveany characters to show positive meaning. However, for the service domain, this word has
a positive meaning. So text preprocessing process for a special lexicon is required. Data
preprocessing task is important to examine the effectiveness and efficiency of customer
analytics in order to make the right decisions in producing new products or services basedon customer satisfaction.
Corpus preparation and lexicon preparation is the main task for extracting theelements of aspects, sub-aspects, and sentiment from unstructured data and thenrearranging them into a structured platform, which will be treated as an attributedimension of the cube. To transform unstructured data to be structured, data require someprinciple for defining a definition of targets. Our task will extract aspects, sub-aspects,
and sentiment, using the total score of customers ‘sentiment definition as above.
4.1. Corpus preparation
The text preprocessing process starts from the crawling procedure to gather usergenerated content from the criticized web-blogs named pantip.com, one of the famous
critiqued websites in Thailand providing free blogs for users who participate in Thai life
insurance. This corpus from web-blogs consists of linguistics terms and non-linguistic
terms such as Thai synonymous words, transliterated words, abbreviations, and slang. So
the process of text preprocessing such sentence segmentation on corpus is required.
Collections of the corpus have different volume data. We made the equal volume of databy preparing a unit group of words on sentence segmentation under conditions. One
divided sentence contains at least one subject and one verb. Divided sentences are
separated from each users’ message in each topic using space and determined length ofThai characters per line around 200-300 characters. The tagged corpus was prepared bysentiment extracted tool, ‘Sansarn tagging tool’ from the previous section. Next process,we did word segmentation, tokenization, and normalization process and then extract key
feature terms of phrases from a given text as Figure 14.
61

4.2. Lexicon Construction
The design and completeness of the lexicon base affect the performance of thesentiment analysis. Lexicon is the terms of a word which found the words matchedin LEXiTRON, Thai-English electronic dictionary provided by NECTEC [56]. The firstkind of lexicon we use to track matching words is similar to a standard dictionary ingeneral meaning. To support a special word’s meaning-extraction, it requires preparing
another kind of lexicon called clue term. They were prepared for special words, phrases,or groups of words that cannot match precisely in LEXiTRON for identifying the group of
aspects/sub-aspects, including sentiment words. For example, the “service mind” word in
the service domain shows positive sentiment on the service touchpoint aspect dimension,“fax claim” shows the claim aspect dimension about fax document but does not have any
senses.
Clue term lexicon preparation in this task divided the lexicons into three types:a.) Lexicon semantics clue Terms:
We arranged this type of clue term by the semantic meaning of linguistic rules in Thai
syntax using a noun, verb, and particular term patterns. Besides, we added non-linguistic
words such as slang words, ambiguous words, and transliterated words.
b.) Complement clue Terms:
For improving the sentences’ semantic, we classified the standard term in many types of
the word, including linguistic and non-linguistic platforms. Linguistic terms containabstract noun (การ, ความ/~ing), preposition (ในช่วง เวลา/during, เกีย่ วกับ/about, ในระหว่างนัน้ /between), auxiliary verb (ควรจะ/should, ต้อง/must), general adverb (วิ เศษณ์ , สุดยอด/excellently,
แย่/terrible) conjunction (ดังนัน้ /therefore, อย่างไรก็ตาม/however, มิ ฉะนัน้ /otherwise) and the otherkind of noun. All of them are terms that do not have any meaning relate to each analysis
group.
c.) Compositional semantics clue terms
These clue terms have a meaning related to pragmatics in words and phrase level. They
consist of technical vocabulary related to life insurance such as การสิ้ นสุดประกันภัย/Lapse, การประกันภัยขยายเวลา/Extended Terms Insurance, สถานะมีผลบังคับของกรมธรรม์ /Inforce, การเรี ยกร้องให้จ่ายเงิน
ตามสิ ทธิ์ ในกรมธรรม์ –เคลม/Claim assesment, กรมธรรม์ใช้เงินสาเร็จ/Paid-up policy, etc.
Besides, we gathered phrases that were negatively or positively felt when expressedin the insurance or service domain. This word’s sense depends on the story, andsometimes they do not have any feelings when they are in a single word. For example, ไม่มี
จรรยาบรรณ/No ethic, ไม่ได้แถลงข้อมูลอันเป็ นข้อเท็จจริ ง/Did not declare the factual information, ยกเลิ กกรมธรรม์ /Reject policy, ปฎิ เสธการจ่ายสิ นไหม/Reject payment, etc.

62

Clue word
Intention
Lexicon :Linguistic
word

Word Typecommon nounปั ญหำกำรเบิกค่ำห ้อง
H&S/hospital benefit
problem (H&S)

abstract noun
transitive/intransitiveverb

Non-linguistic

Phraseปั ญหำทัง้ หลำย/all ofquestions, คำถำมพบบ่อย/frequency quentions-

Regulation

Service Centerควำมเสียใจ/disappointment,ควำมเดือดร ้อน/afflictionประกันแบบสะสม
ทรัพย์/endowment,ประกันแบบชัว่ ครำว/term, ประกันแบบ
ตลอดชีพ/whole lifeเรือ่ งร ้องเรียน/complaint,ค่ำรักษำพยำบำล, medical
expense,กรมกำร
ประกันภัย/department
of insuranceตัวแทน/agent, แผนกเคลม/claim dept.,แผนกบริกำร
ทำงโทรศัพท์/call center,แผนกลูกค ้ำสัมพันธ์/customer service center,หน่วย/agencyแม่ชเี ทเรซ่ำ/Teresanun (represent
good behavior of
people)ตัวเหีย้ ,ตัวเงินตัวทอง, แย ้
(represent bad
behavior of people)่ ผลิตภัณฑ์/ชือ
20TMAE, ค่ำคุ ้มครอง
รักษำพยำบำล/H&S,ค่ำคุ ้มครองโรคร ้ำยแบบพิเศษ/ECIRข ้อบังคับประกันภัย/insurance regulation,กฎเรือ่ งประกันสะสม
เอไอเอ/AIA, ไทยประกัน/ทรัพย์/Endowment
Thaiprakan, เมืองไทย
Rules, สัญญำระยะยำว/ ประกันชีวต
ิ /Moung Thai life
long term contract
insurance--กรมธรรม์ทัง้ หมด/allof policies-ปั ญหำกำรเคลม
ทัง้ หลำย/all of claim
problem-เหล่ำตัวแทน/agent group-คุ ้มครอง/protect,ถอดถอน/withdraw

สำรองจ่ำย/provision,คุ ้มครอง/protectบริกำร/service, สอบถำม/enquiry-

ถำม/ask, สงสัย/wonder,อยำกทรำบ/eager to know, โปรด,พอใจ/love,แนะนำ/advise, กรุณำ/please ไว ้วำงใจ/trust

adjective-

adverb

-Ambigous word, slang
word-

PhraseProduct

Positiveคำถำม/questions,ข ้อสงสัย/ ควำมดีใจ/gladness,doubt, วิธคี ำนวณเบีย้ ประกัน ควำมยินดี, ควำมinsurance premiumเบิกบำน/calculation
cheerfulness

Proper noun

qualitative/collective
noun

Topic
Sentiment

Questionดี/good, เลิศ,ยอดเยีย่ ม/excellent,greatอย่ำงมีสข
ุ /well,อย่ำงดี/properly,
nicelyชิวๆ/chill out, ติส/bohemian style,แอ๊บแบ๊ว/to act
childishlyช่วยแนะนำแบบประกัน
สะสมทรัพย์/please
recommend endowment life มีใจบริกำร/service
insurance product, canmind, ขำดๆเกินๆ/someone tell me?
unbalance

Negative-ปฎิเสธ/reject,ผิดหวัง/disappointed, โกรธ,ฉุนเฉียว/crossยอดแย่/worse, น่ำเบือ่ มำกๆ/betiresome, น่ำรังเกียจ/disgusting

-

-

-

อย่ำงรวดเร็วมำกไป/dramatically, ฉิบหำย/awfully, ถูลถู่ ู
กัง/forcefully, อย่ำง
งุนงง/dazedly

-

-

-

สตรอเบอรี/่ lie, ติงต๊
อง/weird, ควำย/stupid---หำหัวไม่เห็น/cannot
meet, ไม่สำมำรถ
รับได ้/cannot accept,รู ้สึกแย่มำก
เหมือนกัน/feel berybad, โกหกทัง้ เพ/
absolutely lie้ แผนต ้องกำรซือ
ประกันคุ ้มครอง
สุขภำพ20,25 ปี /watto buy 20,25 yearsปกปิ ดควำมจริงเรือ
่ ง
health protection planสุขภำพ, แถลงสุขภำพ
ไม่ตรงควำมจริง

ตัวแทนไม่มใี จบริกำร/agent
does not have service
mind, ปิ ดบังข ้อเท็ จจริง
ของแบบประกัน/conceal thefact of plan

Table 5: Clue terms with linguistic and non-linguistic words example
4.3. Define sentiment scoring by SO-CAL concept
Identifying a polarity of sentiment word is useful for enhancing the power of thesentiment cube. The cube measurement declares that the only word count volume is notenough to show a sentiment severity. Making a score is more useful to get a clear
viewpoint and show more analysis viewpoints. A scoring for sentiment words (lexicon &clue terms) in the additional dictionary was prepared by hand-tagging for giving asentiment lexicon score by applying a scoring concept of Turney named SO-CAL [50].
Semantic Orientation CALculator (SO-CAL) obtains a process of word scoring fromweb-based search. The SO-CAL concept is used to identify the hand-ranked score using
the same five scales for extremely positive and -5 scales for extremely negative, where 0
indicates a neutral word of noun, verb, and adverb. The scoring level expresses on adverb
feeling group shows in SO value column in Table 6. The considerable thing of some partsof speech, such a noun or verb, is that it is possible to contain neutral and non-neutral
connotations. This problem gains some benefits from the hand-tagging method. The
calculation method of the intensifier modifier takes action like an amplifier increase thesemantic intensity of words such as extraordinarily much stronger amplifier than rather.
For example, to define “excellent” has a SO value of 5, Conan's most excellent
comic would set an SO value equal: 5+ (5*100%) = 10. In this work, the negation
handling method is to make it ease by reverse a polarity of term in the lexicon, for63example changing good (+3) into not good (-3). All negation words are included, suchas not, none, nobody, never, nothing, and other else.
Feeling Level

SO-Value

Intensifier

Modifier %excruciatingly-5

somewhat-30%

inexcusable-3

pretty-10%

foolishly-2

really+15%

satisfactorily

1

very

+25%

purposefully

2

extraordinarily+50%

hilariously

4(the)most+100%

Table 6: Sentiment scoring adjustment
When having determined the sentiment word in each sentence, score (si) can be computed
as the sum of scores of the sj is computed score (wj) of all words wj ∈ si multiplied with
their respective weights weight (wj) under the rules of sentiment scoring adjustment:

Score (si) = Σ𝑠𝑐𝑜𝑟𝑒 (𝑤𝑖) ∗ 𝑤𝑒𝑖𝑔ℎ(𝑤𝑗)Example 1: SO-CAL sentiment calculation
Ex 1.1:<Product aspect with negative sentiment>“สิง่ ทีแ่ ย่ ทส่ี ุดคือประกันตลอดชีพ เพราะใช้ เวลานานมาก กว่าจะได้รับทุนประกันคืน”“The worst thing is whole-life plan because it takes long time to get sum assured
return”=>The worst = the most (bad: -5) = -5+ (-5*100%) = -10=>Sentiment score on whole-life plan = -10=>Whole-life plan is clue terms of product aspect
Ex 1.2:<Product topic with negative sentiment>“สิ่ งที่แย่ที่สุดคือประกันสะสมทรัพย์ โดยเฉพาะพวกจ่ ายเบี้ยหลาย ๆ ปี ”
“The worst thing is endowment plan especially, long term payment”=>The worst = the most (bad: -5) = -5+ (-5*100%) = -10=>Sentiment score on endowment product = -10=>Endowment plan is clue terms of product topic<Claim aspect with negative sentiment>“สุดระอา กับการเคลมประกันของบริษัทA””Very fed up with claim process of insurance company A”=>Very = a lot (fed up: -5) = -3+ (-5*100%) = -8=>Sentiment score on claim assessment = -8
Remark: this sentence shows the relationship of claim aspect on company aspect innegative sentiment64

Example 2: SO-CAL sentiment calculation + scoring of compositional semantic clueterms
Ex 2.1:<Claim assessment aspect with compositional semantic clue-term negative sentiment>“ลูกค้ ารู้ สึกไม่ พอใจอย่างมากที่บริษทั ปฎิเสธการเคลมเงินประกัน เพราะทางบริ ษัทได้ พบว่ ามีการปกปิ ดโรคที่เป็ นมาก่อนหน้ า”“Customer got very angry that company had rejected claim payment because company
had found the concealment of the decease record”.
=>very = a lot (angry: -5) = -3+ (-5*100%) = -8=>Sentiment score on claim assessment = -8=>”Rejected claim payment” and “concealment of the decease record” are claim
assessment aspects. The compositional semantics clue terms scoring use in this case.
Both phrases conduce to negative sentiment.
=>Total sentiment score = (-8) + score of “reject payment” + score of“concealment of the decease record”
Ex 2.2:<Claim Problem sub-aspect with compositional semantic clue-term negative sentiment>“บริษทั ไม่ จ่ายค่ารักษาทั้งหมด เพราะทางบริ ษัทได้ สืบค้ นประวัติเจอว่ าคุณแม่ เคยเป็ นโรคมะเร็ งมาก่ อน”“Company cannot pay all of medical fee because company found cancer health recordof mother”.
=>”Company cannot pay all of medical fee” is “claim problem” sub-aspect
related to “cancer health record” claim aspect.
=> Sentiment score = score of “cannot pay all of medical fee” + 0
Remark:Generally, “cannot pay all of the medical fees” does not have any negative feelingword; however, this phrase occurs in the claimed domain. It shows negative sentiment. In
this case, compositional semantic clue-term, which was tagged from Sansarn tagging
tools, is able to support this case. So this phrase show negative sentiment in the ‘Claim’
aspect and score equal to SO-CAL assignment. “cancer health record” does not have any
feeling score; however, this is a keyword to show the relationship of claim problem withcancer decease. Finally, of this step of information extraction, corpus and lexicon are
already prepared to extract features in each aspect from each chunk of a sentence andthen determine sentiment degree on them.
5.

Exploiting Linguistic Knowledge for Sentiment Classification

This part makes an experiment to exploit linguistic knowledge. It aims to improve
topic and sentiment classification in our main corpus for automatically assigningappropriate labels on an appropriate class by training the classification models andlanguage resources using our design clue terms such as linguistic clue term etc., to extract
sentiment from texts. We used some standards supervised automatic text classification
methods such as Naïve Bayes, Decision Tree (J48), and Sequential minimal optimization(SMO) to evaluate the performance. The results from our approach have some
improvement in the accuracy rate.

65

Figure 23: The example of collection of clue terms for intention and topic analysis
As corpus preparation above section, the corpus from web-blogs consists oflinguistics terms and non-linguistic terms, including Thai synonymous words,
transliterated words, abbreviations, and slang. Corpus was prepared by divided sentences
are separated from each users’ message in each topic using space and determined lengthof Thai characters per line around 200-300 characters. The number of divided testing
sentences is 4,668. The tagged corpus was prepared from random discussion topics andwas annotated into three aspects of intention, topic, and sentiment. After that, we do thetokenization and normalization process and then extract key feature terms of phrases
from a given text. (Refer to Figure 23), Lexicon preparation in sentiment classification.
The design and completeness of the lexicon base affect the performance of the sentiment
analysis. Therefore, the corpus preparation process is a necessary process to makeperformance more reliable [7]. We stripped out HTML tags from the corpus and made apart in the chunks of words. The tagged corpus was annotated in three aspects, intention,
topic, and sentiment, to support main analysis components under our method; lexicons
are provided as follows.
The volume of tagging corpus in topic analysis modules (service, product, andregulation) is 46.36%, 7.72%, and 39.48%, respectively. The main lexicon is
LEXiTRON. It is a general word from the Thai dictionary that has 35,933 words.
Particular lexicon volume from this approach (special clue terms and general clue terms)
is 2,439 terms. Sentiment lexicon volume is 1,434 terms.
We experiment with our three kinds of the lexicon, including with Lexicon
semantics clue Terms, Complement clue Terms, and Compositional semantics clue terms.
We demonstrate an automatically assign appropriate labels on an appropriate class bytraining the classification models and language resources manually tagged labels,
including special clue terms construction. Besides, we try to provide more sentiment
analysis by giving a score on special clue terms to indicate the severity level of eachcustomer topic from a cross-relationship of relevant sentences.
Table 7 shows the analysis results of the relationship between main topics in
corpus and lexicon mapping by special clue terms. The hidden relationships in each topic
are revealed in the percentage of the number of mapping words from special clue terms.
The strength of the relationship indicates with high cross percentage. That means they
have a high relationship with customers’ subjectivity. Each row in table 7 is corpus
66

details. There are claim, service, regulation, product, agent, and intention. All of them are
criticized topics in web-blogs. Each column in table 7 is mapping results from all of thelexicons from our approach. The results show the percentage of each lexicon mapping ineach corpus. One corpus can find word mapping with lexicon more than one in eachdivided sentence. Some divided sentences have sentiment words, but some divided
sentences cannot find.
For example of the results from table 7:
Topic Analysis
Intention Analysis
Sentiment Analysis
Service (%)
Regulation (%)
Like
Like
Not Like Not Like
Prod (%)
Ques(%) Inf (%)(Score points)(Score points)
claim agent CSC CCC
REG comp
(%)
(%)
Claim
19.30 5.26 0.00 0.00
7.02 40.35
3.51
8.77
3.51
96
10.53
-18.001.75Service27.61 22.70 0.00 0.61
3.07 19.63
4.91
6.13
0.00
162
7.36
-276.00
7.98
Regulation 14.00 2.39 3.59 1.08
6.34 32.42
7.42
4.19
4.43
1,920
13.76
-1482.00
10.41
Product
16.67 0.00 0.00 0.00
16.67 27.78
2.78
13.89
0.00
114
22.22
0.00
0.00
Agent
0.00 30.73 0.00 0.46
13.30 17.89
5.50
6.88
0.00
636
17.89
-252.00
7.34
Intention
5.79 23.14 0.00 0.00
0.00 21.00
1.65
10.74
0.83
420
19.01
-396.00
17.36
CorpusTable 7: The analysis results in relation in topics, intentions and sentiments
Case A: Agent corpus can find the word mapping in agent lexicon (itself) 30.73%.
An agent has a high impact on company regulation, 17.89%, and product 13.3%.
Case B: Claim corpus can find the word mapping itself 19.30%. A claim has ahigh relationship on company regulation as 40.35%.
Case C: Product corpus can find the word mapping itself 16.67%, and people talkabout the claim process in product corpus 16.67% and talk about company regulation in
product 27.78%. And the results show people ask questions in product corpus 13.89%.
A high percentage of strong relationships should be the first issue is needed
insurance company focuses on. In this analysis, we found that the first important issue isthat company regulation impacts insurance customer activities. The second is the agent's
service to the customer, and the third is a claim issue. For sentiment scores in the like andnot like column shows the problem on service topic and high volume is related tocompany regulation issues. One of the reasons may come from the customer knowledge
in insurance is not enough, so providing enough life insurance knowledge may increasecustomer satisfaction.
The vital thing of in-depth analysis is the annotated special clue lexicons design.
Suppose they provide a sub-level of each issue. In that case, the analysis results mightreveal each problem in detail, so more study in business knowledge is needed to design
special clue terms. To perform an evaluation task, we considered three standard
supervised algorithms: Naïve Bayes, Decision Tree (J48), and SMO. The performance ofeach three methods is compared between baseline results (general) and our methodology
results (general + clue) inaccuracy rate evaluation as in Table 8. Three algorithms are
performed by using 10-fold cross-validations.

67TopicAlgorithm

Term feature
GeneralNaïve Bayes
General + Clue
Decision Tree General
Product(J48)
General + Clue
GeneralSMO
General + Clue
GeneralNaïve Bayes
General + Clue
Decision Tree General
Regulation(J48)
General + Clue
GeneralSMO
General + Clue
GeneralNaïve Bayes
General + Clue
Customer Decision Tree General
Service(J48)
General + Clue
GeneralSMO
General + Clue

Accuracy (%)
93.96
93.83
92.29
92.29
94.34
94.60
68.77
69.41
68.12
69.28
70.95
73.52
75.71
75.06
75.84
76.22
75.45
77.12

Table 8: Accuracy rate of each algorithm
We found some improvement of accuracy rate in almost algorithms, especially
SMO algorithms and except on product topic and customer service topic in Naïve Bayes
algorithm.
6.

Information extraction process from corpus and lexicon

Breaking down opinions into aspect level:
We extracted words according to identified aspects or sub-aspects underhierarchical form based on lexicons and clue terms to generate insightful meaning. The
corpus that was loaded from the websites is unstructured data with the narrative format,
divided into a chunk of sentences, as stated in the previous section.
Figure 24 shows the extraction of words inside a sentence that matching with thelexicon. Start from blog/document level:d split to be sentence level:s and then divided inmatching word/term:t. One chunk of the sentence has some words which were matchedwith the lexicon. That is, sub-aspects under some aspects under a hierarchical structure.
The output of clue terms of aspects/sub-aspects mapping on a chunk of sentences showsthat the adjacent aspects/sub-aspects have the meaning associated with each other.
The results of this step, tracking word matched on the lexicon, we get aspect/subaspect/ (multi-dimension), hierarchical structure (sub-aspect), multi-measurements asstated in the previous section. The number of word counts and sentiment scores were
calculated as SO-CAL formula to present the opinion results breaking down in each
aspect and sub-aspect. The summarization of the score is a group under the same subaspect. We extracted words according to identified aspects or sub-aspects underhierarchical form based on lexicons and clue terms. The corpus that was loaded from thewebsites is unstructured data with the narrative format divided into a chunk of sentences,
as stated in the previous section.

68

Figure 24: Process of aspect/sub-aspects mapping using lexitron dictionary and clueterms
Remark: d is document, s is sentence, t is term, x is clue term, y is score of each clueterm, sn = sentiment score of each chunk
There is a notice of unstructured data tracking with clue terms. The aspect
tracking is unable to match entirely for every aspect on a chunk of a sentence because it
depends on the sentence length limitation. Therefore, it has data incompleteness. A nullvalue was found in the unmatched field on the fact table. However, unmatched fields
remain some associations between the other aspects, so the analysis viewpoint should not
be ignored. On the contrary, we can match multiple clue terms of the same aspect in thesame chunk. It shows that this chunk gives precedence to these aspects more than theothers. Especially, the cube property provides an analysis viewpoint across a different
layer of the hierarchy, so null values of sub-aspect (child level) are able to analyze in
aspect (parent level) for finding a related relationship with the other dimensions.
7.

Design of the Fact tables population

Refer to Figure 17, after we have finished in part of unstructured data preparation by
performing information extraction under aspect-based sentiment analysis. Then data
preprocessing on OLAP is required for the data population process. The general processof data preprocessing such as data cleansing, extract transform, and load (ETL) is
necessary to prepare structured data to be more pattern as analysis purpose for a
populated table in the database called transaction tables. Data was loaded into transaction
tables are the results from the information extraction process. Fact tables are generally
loaded from transaction tables such as order tables or transactional files. Therefore thenumber of rows to update or insert in a load is much larger than in dimensions. The coreof loading fact tables is to change the natural keys into surrogate keys [89]. To re-arrange
data patterns and calculated measurements followed in analysis concepts are required69

structured query language (SQL) and ETL to populated fact tables. The different
viewpoints can transfer from transaction tables to fact tables from a different point ofview by using SQL scripts and operations. The different design of fact tables by SQL
makes the data value of the fact table more meaningful in different ways of analysis.
These fact tables are used for building multidimensional sentiment cube. A data cube is amultidimensional structure of a data visualization that is used in analytical data
warehouses using the Online Analytical Process (OLAP). The design of the fact tabledirectly impacts cube model characteristics, so cube performance and analysis views
depend on population table design. Those of fact tables designate for taking the place of amultidimensional sentiment conceptual model as below.
define lifeINS_cube [company, product, service_touchpoint, service_key_performance,
claim_type, claim_problem, impact_level]:
service_performance_count = sum(service_word_count),
service_performance_score = sum(service_word_score),claim_problem_count=sum(claim_problem_word_count),claim_problem_score= sum(claim_problem_word_score),
impact_level_count = sum(impact_level_count),
impact_level_score = sum(impact_level_score),
negative_count = sum(negative_count),
negative_score = sum(negative_score),positive_count = sum(positive_count),
positive_score = sum(positive_score),sentence_count = sum(sentence_count)define dimension company as (company_name),define dimension product as (Life, Rider, Affinity, special, other)define dimension service_touchpoint as (internal-_service, external_service)define dimension service_evaluation as (reliability, consistency, response, knowledge,
characteristic)
define dimension claim_type as (minor_claim, major_-claim)
define dimension claim_problem as (advance_money, reject_payment, conceal, fax,
long_service, misunder-stand_coverage,misunderstanding_in_protection,
misunderstanding_in_period, misunderstand_in_exception)
define dimension impact_level as (make_sue_on_court, make_petition_for_OIC,
make_accusation_agent, loan,invalid_policy, void_policy, surrender_policy, policy_reject,
policy_cancellation)

In this paper, we performed three fact table design patterns to find out the good
points and weak points from our design examples. Master table is one kind of table thatlooks up as a hierarchical structure. This kind of table is used for joining with transaction
table or fact table for analysis. In our case, we designed a master table that refers to ouranalysis viewpoint, which relates to aspects (parent level) and sub-aspects (child level).
Our master tables compose the table Product, Company, Service Touchpoint, Service
Evaluation Factors Process and Operation, and Dissatisfaction Impact Type as Table 4.

70

Step 3. Utilize and Analysis Results
This step designs three types of a cube as following to utilize a multi-dimensional
sentiment cube to analyze and find knowledge.
Three types are
1. Single aspect analysis (Pattern A)
2. Single Aspect Co-occurrence analysis (Pattern B)
3. Multiple Aspect Co-occurrence analysis (Pattern C)
3.4.1 Single aspect analysis (Pattern A):
id doc_id own_id st_id sap_cdi

sap_cnt i

sap_sc i

1

01

01

01

cd11

cnt11

sc11

2

01

01

02

...cd1m...cnt1m...sc1m

3

01

02

01

cd21

cnt21

sc21

4

01

02

02

...cd2n...cnt2n...sc2n

5

02

01

01

...cdij...cntij...scij

6

02

01

02

ng_cd11…ij ng_cnt11…ij ng_sc11..ij

…j

02

02

03

ps_cd11..ij ps_cnt11..ij ps_sc11..ij

Table 9: An example of fact table aspectPattern_A
This pattern is to analyze aspects by extract a single sub-aspect. The fact table's
key is document id, owner id, sentence id, and sub-aspect code. Unique key of this table
is doc_id||own_id||st_id||sap_cd. In general, one sentence is possible to find matchingwords in some aspects or sub-aspects. If this pattern finds more than one sub-aspect in thesame sentence, the SQL script will generate it by inserting a new row of fact tables. It
means one row of the table has only one sub-aspect to be an element of a concatenatedkey. That’s why a unique key is required sub-aspects to be one key. The primarydimension for analysis is a single sub-aspect column that links with the master table ofaspects. As a hierarchy of master table, the analysis viewpoint will follow the lookup stepon hierarchical from child to parent. Measurements are the number of words count ofeach sub-aspect and score of terms count of each sub-aspect. So the value of multimeasurements can roll-up as a hierarchy of master table.
Example of this case:“อยากยกเลิกกรมธรรม์ของบริษัทAมาก ทาเรื่องเคลมให้ ก็ช้า ตัวแทนประกันไม่ชว่ ยเหลือ ติดต่อหน่วยประกันก็ไม่ได้ สุดท้ ายยังโดน
ปฏิเสธการเคลมอีก”“I would like to make policy cancellation of the company A, make claim process very
slow. Agent did not help. Agency cannot contact. Finally, claim was rejected payment.”“Make policy cancellation” is Medium Impact Type sub-aspect under Dissatisfaction
Impact Type aspect (code ‘PC’, phrase score -5).
“company A” is Company aspect (code ‘comA’, word score = 0)“agent did not help” is Consistency sub-aspect under Service Evaluation aspect (code,‘CONS’, phrase score = -3)71

“Agency cannot contact” is Response sub-aspect under Service Evaluation aspect (code‘’RESP’, phrase score = -3)“claim process very slow” (code ‘CLM’, phrase score = -4) is General Claim > Claim
Assessment under Process and Operation aspect“rejected payment” (code ‘RJ’, phrase score = -5) is Reject Payment sub-aspect>Claim
Problem under Process and Operation aspect
Refer to SQL statement as below.
CREATE fact table aspectPattern_A as
SELECT key_id11…1j, (id, doc_id, own_id, st_id)
CASE WHEN count of sub_aspect11..1m <> 0
THEN ‘sub_aspect11..1m_cd’AS
sap_cdi,sub_aspect11..1m_countAS
sap_cnti,
sub_aspect11..1m_score
AS
sap_sci,
UNION
CASE WHEN count of sub_aspect21..2n <> 0 THEN ‘sub_aspect21..2n_cd’AS
sap_cdi,sub_aspect21..2n_countAS
sap_cnti,
sub_aspect21..2n_score
AS
sap_sci,
UNION
CASE WHEN count of negative11..ij <> 0 THEN ‘negative11..ij_cd’AS
sap_cdi,negative11…1i_countAS
sap_cnti,
negative11…1i_score
AS
sap_sci,
UNION
CASE WHEN count of positive11..ij <> 0 THEN ‘positive11..ij_cd’AS
sap_cdi,positive11…1i_countAS
sap_cnti,
positive11…1i_score
AS
sap_sci,
Repeat CASE of sub_aspect_id = 3…i
FROM populate table

3.4.2 Single Aspect Co-occurrence analysis (Pattern B):
id doc_id own_id st_id sap_cd1 sap_cnt1 sap_sc1 ...sap_cdi …sap_cnt i …sap_sc i ng_cnt ng_sc ps_cnt ps_sc
1

01

01

01

cd11

2

01

01

02

0

3

01

02

01

cd13

4

01

02

02

0

…j

01

02

02

cd1j

cnt11

sc11

0

0

0

ngc1

ngs1 psc1

pss1

0

0

...cdi2…cnti2…sc i2ngc2

ngs2 psc2

pss2

cnt13

sc13

0

0

0

ngc3

ngs3 psc3

pss3

0

0

...cdi4…cnt i4…sc i4ngc4

ngs4 psc4

pss4

cnt1j

sc11j

...cdij…cnt ij…sc ij

ngci

ngsi

pssi

psci

Table 10: An example of fact table aspectPattern_B
This pattern analyzes aspects by extracting a single sub-aspect with remaining theother sub-aspects' structure in the same record with value zero. The fact table's key is
document id, owner id, sentence id, and sub-aspect code. Unique key of this table is
doc_id||own_id||st_id||sap_cd. We used matching words between each sentence andlexicon to identify aspects/sub-aspects, which is a column (dimension) in a fact table.
72

The purpose of pattern B is to keep relation with other sub-aspects in the same record.
Even if the other records have some zero value in some records, the column (aspect) still
can look up as the hierarchy of the master table with some value in that column. Several
multi-dimensions are equal to some analysis sub-aspects. The number of multimeasurements is similar to all of the analysis sub-aspects, which contain the group of
many word counts of each sub-aspect and the score of each sub-aspect. Refer to SQL
statement as below.
CREATE fact table aspectPattern_B as
SELECT key_id1…j, (id,doc_id,own_id,st_id)
CASE WHEN count of sub_aspect1 <> 0 AND count of sub_aspect2…i = 0
THEN ‘sub_aspect1_cd’AS
sap_cd1,
sub_aspect1_countAS
sap_cnt1,
sub_aspect1_score
AS
sap_sc1,
negative1_countASng_cnt1,
negative1_score
AS
ng_sc1,positive1_countAS
ps_cnt1,
positive1_score
AS
ps_sc1,
02,…,0i AS sap_cd2…i,
02,…,0i AS sap_cnt2…i,02,…,0i AS sap_sc2…i
UNION
SELECT key_idj+1…m, (doc_id,own_id,st_id), ‘’ AS sap_cd1, 0 AS sap_cnt1, 0 AS sap_sc1,
CASE WHEN count of sub_aspect2 <> 0 AND count of sub_aspect1 = 0 AND count of
sub_aspect3…i = 0 THEN ‘sub_aspect2_cd’ AS sap_cd2,
03,…,0i AS sap_cd3…i,
sub_aspect2_countAS
sap_cnt2,
sub_aspect2_score
AS
sap_sc2,
negative2_countASng_cnt2,
negative2_score
AS
ng_sc2,
positive2_countAS
ps_cnt2,
positive2_score
AS
ps_sc2,
03,…,0i AS sap_cd3…i,
03,…,0i AS sap_cnt3…i,03,…,0i AS sap_sc3…i
Repeat CASE of sub_aspect_id = 3…i
FROM populate table

3.4.3 Multiple Aspect Co-occurrence analysis (Pattern C):
id doc_id own_id st_id

sap_cnt1

sap_sc1...sap_cdi …sap_cnt i …sap_sc i ng_cnt ng_sc ps_cnt ps_sc

1

01

01

01 cd111||cd112 cnt111+112

sap_cd1

sc111+112…cd i1…cnt i1…sc i1

ngc1

ngs1 psc1 pss1

2

01

01

02

cd12

cnt12

sc12

…cd i2…cnt i2…sc i2ngc2

ngs2 psc2 pss2

3

01

02

01

cd13

cnt13

sc13

…cdi31||cdi32 …cnti31+i32 …sc i31+i32 ngc3

ngs3 psc3 pss3

4

01

02

02

cd14

cnt14

sc14

…j

01

02

02 cd1j 1 ||cd2jK cnt1j 1 +…+ 2jK sc1j1 sc1 j3…cd i3…cnt i4…sc i4ngc4

ngs4 psc4 pss4

…cd ij…cnt ij…sc ijngci

ngsi

Table 11: An example of fact table aspectPattern_C

73

psci

pssi

This pattern is to analyze aspects by extract multiple sub-aspects in the same
records. In tracking multiple-sub-aspects in the same records, sub-aspect code
(column_name) will be represented with new concatenated code. The key of fact tableconsists of document id, owner id, and sentence id. The unique key of this table is
doc_id||own_id||st_id. In this case, sub-aspect will not be a key because one record isrepresented to get value from one sentence.
Refer to SQL statement as below.
CREATE fact table aspectPattern_C as
SELECT key_id11…1j, (id, doc_id, own_id, st_id)[[CASE WHEN count of sub_aspect11 <> 0 AND count of sub_aspect12…1i = 0 THEN
‘sub_aspect11_cd’AS
sap_cd1,
sub_aspect11_countAss
sap_cnt1,
sub_aspect11_score
AS
sap_sc1],[CASE WHEN count of sub_aspect11 <> 0 and count of sub_aspect12..1n <> 0 THEN
‘sub_aspect11_cd’||’sub_aspect12…1n_cd’AS
sap_cd1,
SUM(sub_aspect11…1i_count)Asa
sap_cnt1,
SUM(sub_aspect11…1i_score)AS
sap_sc1,],]SUM(negative11…1i_count)ASng_cnt1,
SUM(negative11…1i_score)ASng_sc1,SUM(positive11…1i_count)AS
ps_cnt1,
SUM(positive11…1i_score)ASps_sc1,
Repeat CASE of sub_aspect_id = 2…i
FROM populate table
Remark:sap_cd1..i is sub-aspect code1..i, sap_cnt1..i is sub-aspect word count1..i, sap_sc1..i is sub-aspectscore1..i, ng_cnt1..i is no.of negative words, ng_sc1..i is score of negative words, ps_cnt1..i is no.of
positive words, ps_sc1..i is score of positive words,
id is record id, doc_id is post/document id, own_id is post woner id,
st_id is sentence_id; j is record_id ; i is aspect_id

Step 4. Sharing
There are many ways of making knowledge sharing. For example, it may prepare
by document, database, or technology in part of explicit knowledge. For tacit knowledge,
co-team setting across co-workers, sharing activities and innovation, learning community,
and job position changes can be utilized for the knowledge sharing process. However, in
this study, we use the method of process sharing using a questionnaire. There are many
previous works to use knowledge sharing by conduct the questionnaire. Several Thai
scholars have examined customer satisfaction with life insurance [66], [74], [94].
However, some gaps remain in the current research, which customer satisfaction
evaluation, using outside-in knowledge, could partially bridge. For instance, even whencompanies implement customer satisfaction improvement strategies, customer complaints
expressing dissatisfaction remain on social media.
Therefore, this research examines the dissatisfaction with Thai life insurance
services and analyzes the severity level of dissatisfaction for each problem. It also
identifies problem-solving methods that customers appreciate. In-depth analysis for74

focus on problem-solving is necessary for the service issues that dissatisfied customers
express on social media. This study analyzes the severity of customer satisfaction withservice dimensions based on negative sentiments on social media using a questionnaire
survey. To the end, it deploys the analytical framework from Figure 25.

Figure 25: Customer Sentiment Analytical framework
We prepared a questionnaire by first tracking the dissatisfaction issues extracted
from negative customer sentiments on social media based on this framework. The“Sentiment extraction tool” was created for tracking negative and positive opinions on
web-blogs on Thai social media site “Pantip.com.” The tools used factor keywords
related to life insurance service in the tracking process [61]. Using the results ofsentiment extraction, we prepared questions on these dissatisfaction issues. The results
identified four dissatisfaction issues: service quality, claim settlements, policy
cancellations, and misunderstandings. All these issues devalue the service in thecustomers’ minds.
In the next step, we analyzed the questionnaires, considering the four
dissatisfaction issues across customer characteristics such as demographics, life insurance
attitude, life insurance experience, and life insurance knowledge. Besides, this survey
included questions about problem-solving methods, which respondents rated the highest.
The implication is that such methods as agreed to by most customers might enableefficient problem-solving based on customer opinions. Using customer engagement to
identify ideas for solving problems is another SCRM concept. After implementing the
problem-solving methods, a customer will shift from an un-valued service perception to avalued one. Ultimately, customer satisfaction could increase. This study conducted thequestionnaire based on sentiment extractions tools and multi-dimensional sentiment cube
analysis results in four main parts, which consists of service evaluation factors, claim
settlement problems, policy cancellation motive, and misunderstanding issues between
customer and service provider.
75

-Service evaluation factors are the service factor dimensions perceived and judged
by customers when receiving such services.
Claim settlement is the insurer's obligation in terms of an insurance contract withthe policyholder (insured) to pay compensation. When the insured's policy covers
the claim, a predetermined amount of money is claimed; namely, the sum assured(S.A.) under the claim assessment principle [44].

-o There are three types of claims in life insurance policies
 Survival Benefit Claim such as Out-Patient Department – OPD, In-Patient
Department – IPD, Hospital Benefit –HB, Critical Illness – CI, Cancer
Benefit – CB, Total Disability, and so on.
 Maturity Benefit Claim Death Benefit Claim-

Policy cancellation is the end of an inforce policy status before the expiration of
the policy. In this case, we review policy cancellation and policy lapses due tocustomer requests.
o Policy cancellation can be done through a request from the policyholder(owner/insurer) or the life insurance company. In some cases, the policyholder
requests policy cancellation due to dissatisfaction with the product or service.
The life insurance company may also cancel the policy if it finds that thepolicyholder has failed to comply with the contract; for example, a customer
conceals health records.
o Policy lapsation occurs automatically when the policyholder discontinues apremium payment, so that all benefits of the policy also stop automatically.
o However, the termination of the policy has a negative effect on both thepolicyholder and the life insurance company. The policyholder loses theinsurance policy's protection, and the accumulated cash value is also lost; the life
insurance company even loses the money due; thus, termination is not profitablefor either party.

-

Misunderstandings of life insurance principles and regulations between
policyholders and service providers include controversial problems that generate
lawsuits or cancellations of life insurance policies, as expressed on social media.

The objectives of the questionnaires conduct are as follows:1.
To measure customer satisfaction with the service evaluation factors to prioritize
the most important problems.
2.
To study the causes of policyholder dissatisfaction with claim settlements, policy
cancellation motives, and reasons leading to misunderstandings with the life insurance
principles and regulations that generates dissatisfaction relevant to users' negative
sentiments on Thai social media.
3.
To study significant factors, such as demographic characteristics, life insurance
experience, knowledge, and attitudes that influence service evaluation factors.
4.
To study the appropriate problem-solving methods for dealing withdissatisfaction.
76

Step 5. Capture and Learning
To capture knowledge sharing between customer sentiment and respondents
who replied to the questionnaire, the results of customer sentiment knowledge sharing
got from work experience, the records of the knowledge apply from knowledge sharing
or knowledge warehouse, etc. To study customer sentiment related to our main target,
we evaluated the questionnaire results under four dimensions of the respondents’qualifications: demographic characteristics, life insurance experience, life insurance
knowledge, and life insurance attitudes of the Thai respondents.
Demographic characteristics:
The demographic characteristic consisted of gender, age, education level, occupation,and income. These were important indicators that help determine the target group for
analysis.
Life insurance experience:
The experience gained from observing, learning and remembering is considered a life
lesson. Each person has a different experience. However, many people can share an
experience together. Learning by experience can become knowledge (20th Century
Chambers dictionary). This study compared respondents who had work experience in life
insurance, such as staff or agents, including those taking a particular class on life
insurance, with respondents who did not have any experience with life insurance.
Life insurance knowledge:
Knowledge is an integration of information based on values, experiences, contextual
information, and expert understanding, providing a framework for evaluating andconsolidating novel information and experiences which creates in the individual minds.
[23]. This study compared the results of respondents who had at least basic knowledgeof life insurance with respondents who did not have such basic knowledge. We
requested that respondents evaluate their own level of understanding, which we tested
through basic questions on their understanding of topics such as premium, sum
assurance, and product (plan) type.
Life insurance attitude:
Attitude is a feeling or opinion about something or toward someone or an expressive
behavior caused by this feeling or opinion. The "attitude-toward-object model" presents
consumer attitudes toward products or brands by evaluating specific product properties'
reliability. When a customer is satisfied with the product or service, he/she believes it has
sufficient qualifications. Therefore, customers will positively evaluate the object;however, if they have a negative attitude and are not satisfied with the product or service.
They will negatively evaluate the product or service (Muhammad et al., 2015) [53]. This
study compared the responses among respondents who had positive, neutral, or negative
attitudes toward life insurance.
Research Method: Customer Sentiment Evaluation Questionnaire
A descriptive research design was adopted to analyze customer satisfaction with Thai
life insurance. This study is based on primary data collected using a questionnaire
77spread via the Google document platform. We created a standard adaptive
questionnaire and separated it into parts A, B, C, D, and E. Part A contained thedemographic characteristics of respondents. Part B contained the life insurance attitudeof the respondents. Part C comprised the basic life insurance knowledge of the
respondents. Part D consisted of the service factors, claims service, policy
cancellations, and misunderstandings. Finally, part E contained an evaluation ofservice improvement methods. The sample size was 374, and suitable statistical tools
such as percentage analysis, average mean score values, and ANOVA at a significanceof 0.05 were adopted. The collected data were analyzed, and the results wereinterpreted as follows.
Data Collection Method
This study's data were acquired from 374 Thai respondents with different demographic
characteristics (gender, age, education, occupation, and income). The respondents had
different life insurance experiences, knowledge of life insurance, and attitudes toward
life insurance. Our questionnaire's target respondents were customers, the insured(policy owners), prospective customers, or non-customers. Table 12 presents the
respondent information.

GenderAge

Frequency

Percent

Frequency

Percent

Female209

55.9

No income

11

2.9

Male

165

44.1

25,000 or less

48

12.8

Total

374

100.0

25,001 - 50,000

139

37.2

20 yrs or less2

0.5

50,001 - 100,000

99

26.5

21 - 30 yrs

31

8.3

100,001 - 200,000

39

10.4

31 - 40 yrs

86

23.0

200,001 or up

38

10.2

41 - 50 yrs

149

39.8

Total

374

100.0

51 - 60 yrs

91

24.7

Have

167

44.7

61 yrs or up

14

3.7

Do not have

207

55.3

Total

374

100.0

Total

374

100.0

23

6.1

Less

69

18.4

230

61.5

Medium

167

44.7

High

138

36.9

Total

374

100.0

Positive

321

85.8

Neutral38

10.2

Less thanBachelor’s
DegreeBachelor’s
DegreeEducation

Income(BTH)

Master’s Degree

111

29.7

Doctorate

10

2.7

Total

374

100.0

Self-employed

53

14.2

Experience

Experience
Knowledge
level

Knowledge
level
AttitudeAttitudeStudent

8

2.1

Negative15

4.0

Employee

259

69.3

Total

374

100.0

26

7.0

Total

374

100.0

28

7.5

374

100.0

Occupation

Government
Official
Retired person/
Unemployed
Total

Table 12: Respondent characteristics
78

Step 6. Create and Leverage
There is a new knowledge asset from the knowledge exchange process using
technology support from all of the processes as state above. This knowledge utilizes tohelp make a decision process of problem-solving method or drives innovation of work
processes from knowledge creation. We are applying learning knowledge and newexperiences to continue to circulate continuously.
Exploiting customer sentiment relate to customer knowledge is one kind of new
knowledge creation. Customer sentiment-related knowledge involved in the interactions
between a company and customers on a social media base can be classified into three
types.
1. Knowledge about customer sentiment – is a kind of knowledge that a company
attains to know the customer's opinion on a product or service, including thecustomer's profile who expresses that feeling. However, such kind of thisinformation, when extracting customer sentiment from social media, the customer
profile is anonymous. The knowledge sharing using questionnaires can fulfill thecustomer profile gap.
2. Knowledge from customer sentiment is the knowledge feedback and theircontributions idea by customers. We can realize what customers feel about
products or services from expressed words on social media from the sentimentextraction tool and sentiment cube. It also reveals the hidden problems andrequires solving the questions very fast if they got very negative sentiment. The
questionnaire is related to those problems we can know why and what customers
think about those problems.
3. Knowledge for customer sentiment is the knowledge a company purposively
provides to customers or the knowledge shared among customers, increasing
customer satisfaction on product or service.
Step 7. Implementation
As the word “The great end of knowledge is not knowledge but action” (DOPA KM
Team, Mason & Mitroff, (1973)) Knowledge dissemination (an implementation) isneeded to achieve the goal which has organized knowledge management activities (KMProcess) and change management process activities simultaneously, with the expectationthat this knowledge management plan will be an important starting point for the
implementation in the scope of Target knowledge management in other matters and leadto a sustainable learning organization.
Step 8. Learning and Monitoring
Monitoring (Plan the next iteration) is an activity that looks forward to testing andevaluating each round's objectives. To follow up on new knowledge creation results toprove this knowledge is sustainable usage and finding new ways to create newknowledge.

79Chapter 4
Experiment Results
The results for answer the research questions are the followings
Main Research Question: How to utilize the knowledge on customer sentiment for
improving customer satisfaction in Thai life insurance?
Sub Research Question 1: How to develop analysis tool for extracting customer
sentiment knowledge from social media?
Sub Research Question 2: What are the issues relate to customer sentiment in Thai
life insurance from knowledge sharing process?
Sub Research Question 3: What kinds of knowledge on customer sentiment havebeen utilize for knowledge sharing process between service provider and customer?
Main Research Question: How to utilize the knowledge on customer sentiment for
improving customer satisfaction in Thai life insurance?
Our knowledge management methodology utilizes the knowledge on customer
sentiment by defining the customer sentiment which obtained from the Outside-In and
Inside-Out management approach to reveal hidden problems of Thai life insurance
service and to get knowledge for finding out the appropriated methods to increasecustomer satisfaction from knowledge sharing process (knowledge co-creation)
between outside-in and inside-out information.
For the outside-in approach, customer sentiment in each service aspect was
retrieved from social media by technology such as text preprocessing, text mining,
natural language processing (NLP), and sentiment analysis. In this stage, wedeveloped the sentiment keyword extraction tool named “Sansarn Tagging tool” andproposed the novel sentiment analysis tool named “Multidimensional sentimentcube” which can provide unlimited viewpoints of analysis by deployed the OLAP
concept. This tool can present deeper analysis by across aspects and sub-aspects thanthe current sentiment analysis approach generally extract in individual aspect level orbasic hierarchical platform.
For the inside-out approach, we do the knowledge sharing process using thesurvey by a questionnaire and interview to collect data in each aspect from specialists,
opinion of customers/non-customers in Thai life insurance service. Knowledge cocreation occurs in this step, the expected results are feedback and idea for problemsolving methods from customers, non-customers and expertise in life insurance.
4.1 Sub Research Answer 1
Sub Research Question 1: How to develop analysis tool for extracting customer
sentiment knowledge from social media?
This research presents a newish concept of analysis tools named multidimensional sentiment cube for extract customer sentiment from social media. It
reveals problems from the service process's significant service issues, which arederived from the relationship among life insurance factors under the social CRM
80

concept. It also used unstructured data from user-generated content in social media
and then analyzed sentiment to identify problems for support service industries suchas life insurance in the social media era. This tools use the traditional concept of
OLAP to make use of benefit in sentiment analysis concept. Also this tools canprovide very complicated point of views from the across operation features ofdimension such as slice-dice, drill-through, drill-down, nested etc. In addition, thissystem also provides the examples of the process to identify the business process that
requires improving due to high negative sentiment from customers using event logs
from the cube.
The Example of Customer Sentiment Extraction from Multidimensional
Sentiment CubeIn the example of analysis, we presented some analysis scenarios from ourmultidimensional sentiment cube on the life insurance functions under the concept ofsocial CRM. This tool revealed customer sentiment knowledge from social media as
following.
Customer Acquisition:
To provide an analysis example to support CRM's customer acquisition strategy thathas the main purpose of approaching new customers and ex-customers. This process
requires a positive attitude of the customer and gain high customer satisfaction. In an
analysis of the life insurance process, we have to clarify a dissatisfaction point ofservice.
Parent (Top level of aspect) analysis:{service_manner||service_evaluation} consider to  {service word count & score,
negative word count & score, positive word count & score, sentence count} Aspect: Service_manner = ‘External service’ has the most negative score ofservice evaluation which can identify Service Manner group with negative score
14.98% of all negative score.
o Non-Match, External service, Internal service and Multi-group get 81.89%,
14.98%, 2.64% and 0.5% respectively.
Child (Sub-aspect) analysis: deep down to find out lower level which related
to parent level Sub-aspect: ‘External service’ = ‘Agent & Agency’ has negative score of service
evaluation on ‘Reliability’>’Consistency’>’Response’>’Characteristic’>’Knowledge’ with negative score = 76%, 8.823%, 7.108%, 6.86% and 0.73%
respectively. (Figure 26)81

Figure 26: An analysis example of customer acquisition 1
In customer acquisition, the most dissatisfaction of customer is ‘reliability’ to agent &agency who is representative to offer new product/plan of life insurance to thecustomer. There are untrustworthy problems on service touchpoints and issues on
inconsistency and irresponsibility, which need to improve to increase customer
satisfaction. As we inform about ‘Non-match’ issue consideration, even if we cannot
map clue-terms of ‘Service manner’ aspect, it can match clue-terms on ‘Service
evaluation’ aspects, and then cube can provide useful analysis view. The results show
the service has problems on ‘Reliability,’ Response,’ ’ Consistency,’ ’Knowledge,’‘Characteristic’ respectively with negative score = 25.98%, 18.04%, 11.58%, 5.45%,
and 3.1% respectively under Non-match group with a negative score 64.13% from all
negative score of Service manner (Figure 27).

Figure 27: An analysis example of customer acquisition 2

82

Customer Retention:
We analyzed aspects/sub-aspects related to life insurance operation on the service
process to support customer retention strategy. Our example made use of the claim
assessment module as below.

Figure 28: An analysis example of customer retention 1
Parent analysis:{claim_problem} consider to  {claim word count & score, negative word count &score, positive word count & score, sentence count} Aspect: Claim problem = ‘Reject Payment,’ ‘Long Service’ and ‘Conceal’ has thehigh negative score of service evaluation with 43.42%, 16.84%, and 13.99% of
total claim problem score respectively. Besides, we found ‘Advance Money’ has ahigh associated with ‘Reject Payment’ in negative sentiment score with 8.91% of
the total claim problem score. It means customer complaints about their frequency
at the same time with high dissatisfaction.
These results show customers’ opinions which are dissatisfied on ‘Reject payment.’ Itmeans customers expect to get the full money price of a medical bill they already paid
to hospital return from an insurance company when they request claim assessment. Ifthey did not meet their expectation, dissatisfaction would occur as a result in Figure
28. As a multidimensional sentiment cube feature, it provides multi-analysis
viewpoints to change the other viewpoints in customer retention. For example, Figure
29 shows customers’ dissatisfaction with claim assessment in the Rider product groupmore than the Life product group. Furthermore, there are high negative sentiments on
misunderstanding point in claim assessment of customer on the benefit of protection(Misunderstanding in protection). The next problem is the period of claim(Misunderstanding in Period).

83

Figure 29: An analysis example of customer retention 2
Parent analysis:{product||Insufficient understanding} consider to  {claim misunderstanding count& score, negative word count & score, positive word count & score, sentence count} Aspect: product = ‘Rider’ and sub-aspect = ‘Insufficient understanding’ has a highnegative score has the most negative score on ‘Misunderstanding in Protection.’o All group of product get negative score on Non-match, Life, Rider, special
product and affinity product 61.12%, 19.20%, 18.55%, 1.06% and 0.07%
respectively.
Customer Termination:
An analysis example of customer termination, we chose multidimensional cubefeatures to find the customers’ sentiment why customers intent to take legal action
proceedings against insurance company using two aspects analysis, Impact_High
related to Claim_Problem aspect as Figure 30.

Figure 30: An analysis example of customer termination 1
84

Child analysis:{Impact-High||Claim_Problem} consider to {imphg word count & score, negative
word count & score, positive word count & score, sentence count} Sub-Aspect: Impact-High = ‘Sue on Law’ related to Claim_Problem = ‘Reject
Payment’ has the most negative score.
 Sub-Aspect: Impact-High = ‘Sue on OIC’ related to Claim_Problem = ‘Reject
Payment’. It means when the insurance company rejects to pay claim assessment
return to the customer. This is one of the main reasons which influence to make apetition for the Office of Insurance Commission.
 Sub-Aspect: Impact-High = ‘Sue on Agent’ related to Claim_Problem = ‘Conceal’has the most negative score.
It means concealed fraud behavior of service touchpoint influences to accuse agent.
An overall negative score of High impact (Exclude non-match) of sue on law, sue on
OIC, sue agent, two aspects group are 42.7%,27.8%, 9.83%, and 19.66% respectively.
Also, we use a multidimensional cube on customers’ sentiment to find out
why customers leave their life insurance contracts. We start to clarify these problems
on Impact_Medium from the parent level, which negatively score on surrender &cancel the policy, surrender the policy, invalid policy and cancel the policy (exclude
non-match) 87.82%, 7.8%, 3.17%, and 1.22%, respectively. Next, we can analyze thechild level, and we found that the ‘Surrender’ problem with ‘Cancel Policy’ has ahigh negative score when the claim problem is ‘Reject Payment’ and ‘Conceal’ Figure
31.

Figure 31: An analysis example of customer termination 2
Sub-Aspect: Impact-Medium in (‘Surrender', ’Cancel Policy’) related to
Claim_Problem in (‘Reject Payment', ’ Conceal’) has the most negative score.

4.2 Sub Research Answer 2
Sub Research Question 2: What are the issues relate to customer sentiment inThai life insurance from knowledge sharing process?
To summarize the results of the customer sentiment extraction related to issuesin Thai life insurance regarding customer sentiment, we analyzed customer
acquisition, customer retention, and customer termination from multidimensional85

sentiment cube found four important issues as the following
1. Service evaluation factors: The results found that dissatisfaction issues relate to
the reliability, knowledge, responsiveness, service consistency, and personalityof a service provider.
o Reliability - ability to provide services agreed to in the contract.
o Knowledge - the capability to provide efficient knowledge.
o Responsiveness – readiness and willingness to provide services that
respond to customers' needs promptly.
o Service consistency - enabling customers to feel that the service isreliable and can be trusted.
o Personality - ability to provide services with courtesy, politeness, andgood manners, including effective communications.
2. Claim settlemento The dissatisfaction factors relevant to claim settlements on social media
found from the analysis tool were as follows:- Taking a long time to compensate for the policyholder.
- Receiving lower compensation than the policyholder’s expectation.
- Policyholder’s claim is rejected (does not receive compensation).
- The policyholder is required to pay advance health expenses to thehospital.
- The policyholder does not understand the claim process.
- Concealing the health record of the policyholder.
- Policyholder cannot contact service provider for support during theclaim process in a timely manner.
3.

Policy cancellation the results found the policy cancellation related tocustomer request to cancel the policy on their hands, including policy lapses
because a customer did not continue premium payment for many reasons. For
example, customers request policy cancellation because of dissatisfaction inservice, policy on hand does not meet the fundamental requirement, and thecustomer's financial status has changed. It cannot continue to pay a premium atthe same rate as previous. In addition, the results found that the customers
prefer to other life insurance companies rather than current one, and theywould like to make a new contract and to reject the current policies.
Sometimes, the results found customers gave their opinion about policy
cancellation because they require sum assured before the policy maturity
period.

4. Misunderstandings of life insurance principles and regulations between
policyholders and service providers include controversial problems thatgenerate lawsuits or cancellations of life insurance policies, as expressed onsocial media. The topics found were as follows.
o Policy regulations related to rejected claimso Service provider issues such as agent reliabilityo Lack of knowledge of the service provider86

o Lack of prudence by the service providero Uncontrolled situations, such as economic instability, changing the total
sum assured at policy maturity.
4.3 Sub Research Answer 3
Sub Research Question 3: What kinds of knowledge on customer sentiment havebeen utilize for knowledge sharing process between service provider andcustomer?
As knowledge is what is accumulated from education, research, or experience,
including practical ability and skills, understanding, or information gained fromexperience, gained through hearing, hearing, thinking, or practice in a specific field ofstudy (https://www.gotoknow.org/posts/396638). To create customer sentimentknowledge for answering our objectives, we integrate the results of customer
sentiment from multidimensional sentiment cube analysis with the results from survey
using questionnaire.
Customer sentiment knowledge, which is stated, has two kinds of customer
sentiment knowledge sources. It was generated from two ways communication
between service providers and customers on social media and the questionnaire.
The first kind of customer sentiment knowledge is 'Outside-in sentimentknowledge.' It includes 'Knowledge of customer sentiment' and 'Knowledge from
customer sentiment' as stated in the previous section. Knowledge of customer
sentiment is related to customer sentiment, which expresses social media's
dissatisfaction with claim settlement policy cancellation and misunderstanding
issues. Knowledge from customer sentiment is the dissatisfaction level fromnegative scoring and reveals the significant of hidden problems with prioritizedsentiment scores.
The second kind of customer sentiment knowledge is ‘Inside-out sentiment.’ It is‘Knowledge for customer sentiment.’ Service provider expertise offers problemsolving methods for dissatisfaction problems, and respondents gave scoring in thequestionnaire. The results showed the appreciated problem-solving approach bycustomers.
1. Outside-in sentiment knowledge: Knowledge of customer sentiment andKnowledge from customer sentiment.
From sub research answer 2, we have got the customer sentiment, which expresses
social media's dissatisfaction in service with claim settlement policy cancellationand misunderstanding issues. To integrate and confirm the results between the
results from our tools and the survey, we summarize and analyze the answers
according to the data's significant factors from the questionnaire using SPSS. The
questionnaire's evaluation scores used a five-point scale, with one representing leastdissatisfied and five representing most dissatisfied. The results of each objective areas follows.
The results from survey in part of Service evaluation factors:
87

Objective A: to measure customer dissatisfaction with the service evaluation factorsof the service providers to prioritize the most important problems.
This study analyzed dissatisfaction based on lack of reliability-LackReliability,
lack of knowledge-LackKnowledge, lack of responsiveness–LackResponsiveness,lack of consistency in service-LackConsistency, lack of good personalityLackPersonality based on life insurance experience, knowledge, and attitudes.
Experience

Knowledge

AttitudeService
EvaluationExp

No_ExpHigh

Medium

Less

POS

NEU

NEG

LackReliability

2.868

2.802

2.870

2.856

2.696

2.804

3.211

2.467

2.832

LackKnowledge

2.874

2.768

2.862

2.796

2.768

2.810

2.868

2.800

2.816

LackResponsiveness

2.898

2.826

2.935

2.814

2.812

2.829

3.237

2.533

2.858

LackConsistency

2.988

2.821

3.014

2.850

2.768

2.872

3.211

2.600

2.896

LackPersonality

2.695

2.729

2.725

2.701

2.725

2.713

2.711

2.733

2.714

Total

Table 13: Measures of customer dissatisfaction on service evaluation factors

Table 13 presents the results of the average mean score values. We found that themost dissatisfied service evaluation factors were “lack of consistency in service”
and “lack of responsiveness” based on experience (2.988, 2.898), high knowledge(3.014, 2.935), and positive and neutral attitudes (2.872, 2.829, 3.237, and 3.211).
However, the respondents with negative attitudes gave the highest dissatisfied
scores to knowledge (2.8) and personality (2.733).
Objective B. to study significant factors that influence service evaluation factors.
This study analyzed dissatisfaction in terms of service evaluation factors
consisting of lack of reliability-LackReliability, lack of knowledge-LackKnowledge,lack of responsiveness–LackResponsiveness, lack of consistency in serviceLackConsistency, lack of good personality-LackPersonality based on demographic
characteristics (gender, age, education, occupation, and income) and life insurance
experience, knowledge and attitudes. The results are an analysis of the significant
factors using SPSS and ANOVA tests at a significance level 0.05.
ANOVA
Dissatisfaction
with service

Significant
factors

LackReliability

Education

LackReliability

Attitude

LackKnowledge

Education

LackResponsive

Age

LackResponsive

Education

BetweenGroups
BetweenGroups
BetweenGroups
BetweenGroups
BetweenGroups88

Sum ofSquaresdf

Mean
Square

F

Sig.

8.945

3

2.982

4.319

0.005

8.283

4

2.071

2.983

0.019

7.690

4

1.922

2.786

0.026

17.770

5

3.554

4.250

0.001

12.969

3

4.323

5.118

0.002

LackResponsive

Attitude

LackConsistency

Age

Avg. of Service
dissatisfaction
Avg. of Service
dissatisfaction

Age
Education

BetweenGroups
BetweenGroups
BetweenGroups
BetweenGroups

8.894

4

2.223

2.592

0.036

13.447

5

2.689

2.848

0.015

10.269

5

2.054

4.226

0.001

4.378

3

1.459

2.923

0.034

Table 14: ANOVA results

Lack of reliability:
The results indicate that the lack of reliability does vary significantly according to
education and attitude as Table 14 shows.
 Education. Test of Homogeneity of Variances: Levene = 0.919 and Sig. =0.432 more than alpha (0.05), so accept H0, F-test
F-test = 4.319 or Sig. = p-value = 0.005, less than 0.05 (α). The null hypothesis(H0) is rejected. Therefore, dissatisfaction with reliability does vary significantly
according to education.
 Attitude. Test of Homogeneity of Variances: Levene = 1.159 and Sig. = 0.329
more than alpha (0.05), so accept H0, F-test
F-test = 2.983 or Sig. = p-value = 0.019, less than 0.05 (α). The null hypothesis(H0) is rejected. Therefore, dissatisfaction with reliability does vary significantly
according to attitude.
Lack of knowledge:
The results indicate that a lack of knowledge does vary significantly according to
occupation, as Table 14 shows.
 Occupation. Test of Homogeneity of Variances: Levene = 0.919 and Sig. =0.453 more than alpha (0.05,) so accept H0, F-test
F-test = 2.786 or Sig. = p-value = 0.026, less than 0.05 (α). The null hypothesis(H0) is rejected. Therefore, dissatisfaction with lack of knowledge does varysignificantly according to occupation.
Lack of responsiveness:
The results indicate that a lack of responsiveness does vary significantly according
to age, education, and attitude, as Table 14 shows.
 Age. Test of Homogeneity of Variances: Levene = 0.554 and Sig. = 0.735 morethan alpha (0.05), so accept H0, F-test
F-test = 4.25 or Sig. = p-value = 0.001, less than 0.05 (α). The null hypothesis (H0)is rejected. Therefore, dissatisfaction with responsiveness does vary significantly
according to age.
 Education. Test of Homogeneity of Variances: Levene = 0.554 and Sig. =0.753 more than alpha (0.05), so accept H0, F-test
F-test = 5.118 or Sig. = p-value = 0.002, less than 0.05 (α). The null hypothesis(H0) is rejected. Therefore, dissatisfaction with responsiveness does vary89

significantly according to education.
 Attitude. Test of Homogeneity of Variances: Levene = 1.061 and Sig. = 0.376
more than alpha (0.05), so accept H0, F-test
F-test = 2.592 or Sig. = p-value = 0.036, less than 0.05 (α). The null hypothesis(H0) is rejected. Therefore, the dissatisfaction in responsiveness does varysignificantly according to attitude.
Lack of consistency in service:
The results indicate that the lack of service consistency over the long term doesvary significantly according to age, as shown in Table 14.
 Age. Test of Homogeneity of Variances: Levene = 0.482 and Sig. = 0.789
more than alpha (0.05), so accept H0, F-test
F-test = 2.848 or Sig. = p-value = 0.015, less than 0.05 (α). The null hypothesis(H0) is rejected. Therefore, dissatisfaction with consistency of service does varysignificantly according to age.
Average of service dissatisfaction factor:
The results found that the service evaluation factors (average mean score) do varysignificantly according to age and education as shown in Table 14.
Age. Test of Homogeneity of Variances: Levene = 0.599 and Sig. = 0.701
more than alpha (0.05), so accept H0, F-test
F-test = 4.226 or Sig. = p-value = 0.001, less than 0.05 (α). The null hypothesis(H0) is rejected. Therefore, the average mean score of the service evaluation factors
does significantly vary according to age.
 Education. Test of Homogeneity of Variances: Levene = 1.633 and Sig. =0.181 more than alpha (0.05), so accept H0, F-test
F-test = 4.378 or Sig. = p-value = 0.034, less than 0.05 (α). The null hypothesis(H0) is rejected. Therefore, the average mean score of the service evaluation factors
significantly varies according to education.
Causes of policyholder dissatisfaction with claim settlements:
This study analyzed the causes of policyholder dissatisfaction with the claim
settlement process, which consisted of cannot contact service provider–
ClmCNContact, get compensation in long term–ClmGetCompLngT, cannot makeclaim (for that decease)–ClmCNClaim, pay medical expense in advance–
Clm_PayMedExpAv, found conceal health record of customer–ClmConceal basedon life insurance experience, knowledge and attitudes.
Experience

Knowledge

AttitudeService
EvaluationExp

No_ExpHigh

Medium

Less

POS

NEU

NEG

ClmCNContact

2.616

2.613

2.710

2.569

2.536

2.589

2.684

3.000

2.615

ClmGetCompLngT

2.778

2.719

2.833

2.665

2.768

2.701

2.868

3.400

2.746

ClmCNClaim

2.539

2.497

2.572

2.491

2.464

2.477

2.684

2.933

2.516

90

Total

ClmPayMedExpAv

2.988

2.801

3.087

2.749

2.812

2.844

3.026

3.400

2.885

ClmConceal

2.623

2.522

2.667

2.491

2.551

2.539

2.737

2.733

2.567

Table 15: Analysis of causes of policyholder dissatisfaction with claim settlement process

Table 15 presents the results of the average mean score values. We found the same
results for three of the analysis bases (life insurance experience, knowledge andattitudes) with the highest policyholder dissatisfaction with claim settlements as first,“pay medical expense in advance” (2.988, 2.801, 3.087, 2.749, 2.812, 2.844, 3.026,
and 3.4) and second as “get compensation over long term” (2.778, 2.719, 2.833,
2.665, 2.768, 2.701, 2.868, and 3.4). This implies that policyholders do not want toreserve money to pay medical expenses in advance or obtain late compensation.
Thus, they were dissatisfied with the time taken to receive compensation. However,
it is necessary to focus on other dissatisfaction-related problems also. The inability
to contact the service provider, concealing health history records, and inability tomake a claim received dissatisfaction scores that were similar to the scores of bothmain problems.
Policyholder dissatisfaction with policy cancellation:
This study analyzed the motive of policyholder dissatisfaction with policy
cancellation, consisting of get unfavorable service–CancUnfovServ, policy does notmeet requirement–CancPolNotReg, financial status is changed–CancFinChg, needto change Life insurance company–CancChgCom, need sum assured before policy’s
maturity period–CancReqSumAs based on life insurance experience, knowledge,
and attitude.
Experience

Knowledge

AttitudeService
EvaluationExp

No_ExpHigh

Medium

Less

POS

NEU

NEG

CancUnfovServ

3.114

3.360

3.210

3.239

3.353

3.237

3.289

3.400

3.249

CancPolNotReq

3.169

3.316

3.210

3.211

3.426

3.232

3.368

3.333

3.250

CancFinChg

3.491

3.495

3.471

3.485

3.559

3.484

3.553

3.533

3.493

CancChgCom

2.729

2.937

2.674

2.946

2.941

2.824

2.947

3.000

2.844

CancRegSumAs

3.156

3.239

3.261

3.145

3.221

3.172

3.237

3.733

3.202

Total

Table 16: Analysis of policyholder dissatisfaction with policy cancellations

Table 16 presents the results of the average mean score values. We found the same
results for the three analysis bases (life insurance experience, knowledge, andattitudes, except negative attitude). The highest ranked motive for policy
cancellation was financial status is changed (3.491, 3.495, 3.471, 3.485, 3.559,
3.484, 3.553 and 3.533), followed by policy does not meet requirement and get
unfavorable service.
As most life insurance plans support policyholders over the long-term, thatis, more than ten years, twenty years, or whole life, policyholders have to pay
premiums over the long term as well. If the policyholder's financial status changes, it
becomes difficult to maintain the premium payments, making this a strong91

motivation to cancel the life insurance policy. Hence, service providers must
consider plan adjustments over a long-term service contract. The next motivationreferenced for policy cancellation was that policy does not meet the requirement.
The agent should consider this problem at the product offer stage before thecustomer enters the policy contract to ensure the life insurance plan can meet thecustomer's requirement. The other main motive was to get unfavorable service,
which was also shown as a strong motive for policy cancellation.
However, the responses of those who had negative attitudes toward life
insurance were quite different from others' reactions. They indicated their mostserious concern as "need sum assured before the policy's maturity period." It implies
that they might not be satisfied with depositing their money in a life insurance productover the long term and believe such a large sum assured could provide more benefits
in other ways.
Reasons for misunderstanding of life insurance principles and regulations:This study analyzed the reasons leading to misunderstandings regarding life
insurance principles and regulations between policyholders and service providers. It
consists of misunderstanding regarding many conditions–MisCondition,
misunderstanding because of agent informing only about the benefits–
MisAgtInfrmBen, misunderstanding owing to agent lack of carefulness–
MisAgtLackCare, economic change impacting need for change–MisEcochgBenchg,
agent does not have enough knowledge–MisAgtNoKnwldg based on life insurance
experience, knowledge, and attitudes.

Experience

Knowledge

AttitudeService
EvaluationExp

No_ExpHigh

Medium

Less

POS

NEU

NEG

MisCondition

3.635

3.778

3.638

3.665

3.986

3.667

4.000

4.000

3.714

MisAgtInfrmBen

3.612

3.699

3.609

3.581

3.797

3.570

4.026

3.933

3.660

MisAgtLackCare

3.533

3.670

3.507

3.599

3.783

3.579

3.605

4.000

3.609

MisEcochgBenchg

3.263

3.377

3.246

3.317

3.507

3.312

3.342

3.600

3.326

MisAgtNoKnwldg

3.413

3.359

3.384

3.323

3.478

3.352

3.447

3.667

3.383

Total

Table 17: Analysis of misunderstandings regarding life insurance principles and regulations

Table 17 presents the results of the average mean score values. We found the same
results for three of the analysis bases (life insurance experience, knowledge, andattitude), namely, that the most common reason for misunderstandings of life
insurance principles and regulations was the misunderstanding regarding many
conditions (3.635, 3.778, 3.638, 3.665, 3.986, 3.667, 4.0, and 4.0). The next was amisunderstanding because the agent only informed about the benefits andmisunderstanding due to an agent's carefulness. This feedback implies that many life
insurance conditions and regulations are easily misunderstood, mainly when agents
discuss only the policy's benefits to sell the plan. Moreover, agents may not provide
clear information about the conditions of the policy. The final benefits, such as thesum assured based on the provider, dividend, or interest, may vary as economic
situations change, leading to further misunderstandings. Hence, life insurance is92

complicated and requires agents with sufficient knowledge to provide information tocustomers before the latter sign policy contracts.
2. Inside-out sentiment: Knowledge for customer sentiment
Service provider expertise offers problem-solving methods for dissatisfaction
problems, and respondents gave scoring in the questionnaire. The results showed theappreciated problem-solving approach by customers.
Objective: to study the appropriate problem-solving methods for dissatisfaction
problems.
This study analyzed appropriate problem-solving methods that respondents
were optimistic about, consisting of discussions to solve problems–FixDiscuss,changes in policy types–FixChgPolTyp, consulting with mediating organizations–
FixConOrg, issuing premium discounts–FixPremDisc, and offering a gift topolicyholders–FixGift based on life insurance experience, knowledge, and attitudes.
Experience

Knowledge

AttitudeProblemSolvingExp

No_ExpHigh

Medium

Less

POS

NEU

NEG

FixDiscuss

3.802

3.652

3.841

3.629

3.696

3.698

3.842

3.867

3.719

FixChgPolTyp

3.569

3.488

3.594

3.467

3.522

3.520

3.684

3.200

3.524

FixConOrg

3.551

3.372

3.580

3.473

3.565

3.533

3.526

3.467

3.529

FixPremDisc

3.108

3.377

3.116

3.287

3.449

3.224

3.368

3.600

3.254

FixGift

3.072

3.242

3.138

3.156

3.246

3.143

3.263

3.400

3.166

Total

Table 18: Analysis of appropriate problem-solving methods for each service problem

All of the base analysis qualities (experience: has/does not have, knowledge:
high/medium/low, attitude: positive/neutral/negative) showed the same results withrespondents giving the problem-solving method discussion to solve problems thehighest score: 3.802, 3.652, 3.841, 3.629, 3.696, 3.698, 3.842, and 3.867 out of
5 respectively. The second-highest ranked problem-solving method was consultingwith mediating organizations. The third-ranked method was allowing a change inpolicy type. However, respondents with negative attitudes held a different
perspective, giving discount the premium a score of 3.6 out of 5. Furthermore,
although giving gifts to policyholders did not get the top score, it did receive a highscore similar to the others.
4.4 Multidimensional Sentiment Cube Evaluation by domain experts and endusers
Reviewing the usage of multidimensional sentiment cube with domain experts
who have experience in the Life insurance domain is necessary for understanding
their opinion on System Design, System Usage, and System Validation for future
improvement. This research interviewed domain experts in Life insurance and theother domain such as telecommunication, retail, and consultant domain. Their work
positions consist of management level and general users, and some interviewees havecomputer skills, and some have less computer skill. The total number of theinterviewee is thirteen persons. We evaluate three parts. The first is system design, the93second is system usage, and the last is system validation. The results show in the tablebelow.
a. Does multidimensional sentiment cube or sentiment analysis have already
implemented in your organization?

Implement
Not implement yet
under study

Domain Expert
IT(5)Non-IT(2)52-Other Domain
IT(4)Non-IT(2)
3
2
1
-

Total
13
0
12
1

Table 19: An interview results 1
We found almost all organizations of interviewees are not start to implement any
application related to sentiment analysis, including sentiment cube 92.3%. However,some organization begins to study the possibility of a sentiment system at 7.69%.
b. In your opinion, who is proper to use this system?

a. domain expert in insurance
b. domain expert in CRM
c. business analysis
d. IT
e. management levelf. strategic plannerg. user in organizationh. general user & customer

Domain Expert
IT(5)Non-IT(2)
3
2
5
2
3
2
2
2
5
2
5
2
0
2
0
2

Other Domain
IT(4)Non-IT(2)
2
2
2
2
3
2
2
2
3
2
2
2
0
0
0
1

Total
13
9
11
10
8
12
11
2
3

%
69.23
84.62
76.92
61.54
92.31
84.62
15.38
23.08

Table 20: An interview results 2
Interviewees give opinions that this application proper to Management level ofcompany 92.31%. It means the results of this application can help management level
make a decision, and the next is a strategic planner and domain expert in CRM on
84.62%. In the interviewees’ opinion, they agree to use these results to set up thecompany’s strategies and help support information to the CRM system. They agree
that this application is proper to business analysis that provides analysis report tomanagement level on 76.92%. The next is a domain expert in insurance, and IT on
69.23% and 61.54% respectively.

94

Figure 32: The industry positions who proper to use this system
System Design:
100%
D.E.
IT(5)
NIT(2)
1 Do you think the concept of sentiment
analysis can help identifying the customers'
dissatisfaction from social media for CRM?
2 Do you think CRM concept can help
identifying aspect for design?
3 Do you think process flow can help
identifying weak points in current process?
4 Do you agree with Aspect design in thisexperiment?
5 Do you agree with measurement in thisexperiment by using word cnt and sentimentscoring?

IT(4)

80%
50%
30%
0%
D.E.
O.D.
D.E.
O.D.
D.E.
O.D.
IT(5) NIT(2) IT(4) NIT(2) IT(5) NIT(2) IT(4) NIT(2) IT(5) NIT(2) IT(4) NIT(2)O.D.
NIT(2)

0

0

1

2

4

2

2

0

1

0

1

0

0

0

0

0

0

0

1

1

2

5

1

1

0

0

0

2

0

0

0

0

0

0

3

1

1

2

2

0

3

0

0

1

0

0

0

0

0

0

0

3

0

3

2

2

2

1

0

0

0

0

0

0

0

0

0

0

2

3

3

2

3

1

0

0

0

0

1

0

0

0

0

0

0

Table 21: An interview results of system design 1
Remark: D.E. (domain expert) is an interviewee, who is domain expert in life insurance industry, O.D.
(other domains) is an interviewee, who works in other domains not related to life insurance, IT is aninterviewee, who works relate to information technology, NIT (non-IT) is an interviewee who works
not relate to information technology
Average (%)
D.E.
O.D.
IT(5)
NIT(2)IT(4)NIT(2)
System Design

System Design
System Design
System Design
System Design

1 Do you think the concept of sentiment
analysis can help identifying the customers'
dissatisfaction from social media for CRM?
2 Do you think CRM concept can help
identifying aspect for design?
3 Do you think process flow can help
identifying weak points in current process?
4 Do you agree with Aspect design in thisexperiment?
5 Do you agree with measurement in thisexperiment by using word cnt and sentimentscoring?

74

80

77.5

100

80

90

70

100

92

75

85

100

92

80

95

100

88

95

87.5

100

Table 22: An interview results of system design 295

Figure 33: The evaluation of system design
The purpose of the interview about system design is to confirm the sentiment analysis,
CRM concept, and BPMN – business process flow concept is acceptable for real
analysis.
- Sentiment analysis is agreed to support the CRM concept at least 74% by thedomain expert (IT) and the most acceptance by other domains (non-IT) 100%.
- CRM concepts are agreed to help identify aspects at least 70% by otherdomains (IT) and the most accepted by other domains (non-IT) 100%.
- Process flow is agreed for identifying weak points in the current process atleast 75% by the domain expert (non-IT) and the most acceptances by otherdomains (non-IT) 100%.
- Aspect design in this experiment is accepted at least 80% by the domain
expert (non-IT) and the most acceptance by other domains (non-IT) 100%.
- The measure of this system use word count and word with sentiment scoring
are accepted at least 87.5% by other domain (IT) and the most acceptance byother domains (non-IT) 100%.
Recommendation: There is a recommendation to use emoticon or stickers for
sentiment measurement in this system (Interviewee#9). Some interviewees
recommend calculating the sentiment scoring variance in statistical terms withsentiment word scoring (Interviewee#5).
System Usage:
100%
80%
50%
30%
0%
D.E.
O.D.
D.E.
O.D.
D.E.
O.D.
D.E.
O.D.
IT(5) NIT(2) IT(4) NIT(2) IT(5) NIT(2) IT(4) NIT(2) IT(5) NIT(2) IT(4) NIT(2) IT(5) NIT(2) IT(4) NIT(2)1 Can the results of multidimensional sentimentcube express the relation among features of thedissatisfaction which get from social media?
2 Can the results of data mining rule express thedissatisfaction which get from social media more
concretely?

0

1

3

2

5

1

0

0

0

0

1

0

0

0

0

0

1

0

3

2

3

1

0

0

0

1

1

0

1

0

0

0

96

Table 23: An interview results of system usage 1
Average (%)
D.E.
O.D.
IT(5)
NIT(2)IT(4)NIT(2)
System Usage

System Usage

1 Can the results of multidimensional sentimentcube express the relation among features of thedissatisfaction which get from social media?
2 Can the results of data mining rule express thedissatisfaction which get from social media more
concretely?

80

90

87.5

100

74

65

87.5

100

Table 24: An interview results of system usage 2

Figure 34: The evaluation of system usage
The purpose to interview about system usage is to make confirmation on the useful ofmultidimensional sentiment cube and data mining rules.
-

Multidimensional sentiment cube can express the relation among features ofdissatisfaction which get from social media, interviewees are agreed at least
80% from domain expert (IT) and the most acceptance by other domains (nonIT) 100%.

-

Data mining rules can help express dissatisfaction of customer from social
media more concretely are agreed at least 65% from domain expert (non-IT)
and the most acceptance by other domains (non-IT) 100%.

97

System Usage

3 Do you prefer to use multidimensional sentiment cube for analysis ?

a. Prefer and can analysis by yourself
b. Prefer but cannot analysis by yourself
c. Not prefer
System Usage

Domain Expert
IT(5) Non-IT(2)
5
2
0
0
0
0

Other Domain
IT(4) Non-IT(2)
4
0
0
2
0
0

%
84.62
15.38
0.00

4 Do you prefer to use data mining rules for setting up the dependency of severity tasks to solve problems?
Domain Expert
Other Domain
IT(5)Non-IT(2) IT(4)Non-IT(2)%a. Prefer
4
2
4
2
92.31
b. Not prefer
1
0
0
0
7.69

Table 25: An interview results of system usage 3

Figure 35: The evaluation on system validation
To survey the difficulty of application usage by asking the preferable usage of cube
operation with business knowledge, we found that 84.62% prefer to use thisapplication and confidence to analyze by themselves more than request other people
to analyze for them, which have 15.38% (other domain, non-IT).
System usage: What difference between multidimensional sentiment cube usage anddata mining rules usage in your opinion?
Answer:“Multidimensional sentiment cube can answer all of the end-users' questions
depending on their requirements; however, data mining rules can change analysis
trends and prediction. (Interviewee#1)”,“The result of multidimensional sentiment cube and data mining rules is the same; it
depends on the requirements of analysis viewpoints; however, the statistical value issignificant, mostly negative values. (Interviewee#2)”,98

“A multidimensional sentiment cube is present in the relational database; however,data mining is for a summary. (Interviewee#3)”,“Multidimensional sentiment cube can present in death analysis to serve many
analysis requirements. It can represent the abstract value such as satisfaction anddissatisfaction with substantial values; however, data mining is present in terms of
substantial values only, yet more concretely. (Interviewee#4)”,“Multidimensional sentiment cube is easy for analysis in multi-viewpoints, it shows interm of the relationship of features, so it is relatively easy for understanding andsuitable for represent the results of data, data mining rules in particular however it
can inform the significant of information (Interviewee#5)”.
“Multidimensional sentiment cube shows the relation between features that answer
why those problems are occurred, more flexible in analysis term and meet therequirement of analysis ways as end-users want. Data mining rules also provide trend
and concrete rules in specific analysis (Interviewee#6)”,“I prefer a multidimensional sentiment cube because it shows figures in all viewpoints
which easy to understand and comparable; however, data mining rules are a dataconclusion in a feeling of end-users. (Interviewee#7)”,“Multidimensional sentiment cube shows the death analysis viewpoints and data
mining rules show the significance of data, so I prefer to use both of information(Interviewee#8)”.
“Multidimensional sentiment cube use for analysis in many viewpoints of analysis
however data mining rules use in specific analysis (Interviewee#9)”,"Multidimensional sentiment cube use for analysis group or categorize, data
mining(association rule) can know the relationship of each aspect with their direction(Interviewee#11)"

System Validation:
100%
D.E.
IT(5)
NIT(2)
1 This result is for?
a. Confirm your knowledge
b. New knowledge
2 Can these results help identifying
the process which need to improveon dissatisfaction problem?

IT(4)

80%
50%
30%
0%
D.E.
O.D.
D.E.
O.D.
D.E.
O.D.
D.E.
O.D.
IT(5) NIT(2) IT(4) NIT(2) IT(5) NIT(2) IT(4) NIT(2) IT(5) NIT(2) IT(4) NIT(2) IT(5) NIT(2) IT(4) NIT(2)O.D.
NIT(2)

4
1

2
0

1
3

0
1

1
3

0
2

1
0

1
1

0
1

0
0

1
0

0
0

0
0

0
0

0
1

0
0

0
0

0
0

1
0

1
0

1

0

2

2

3

2

2

0

1

0

0

0

0

0

0

0

0

0

0

0

Table 26: An interview results of system validation 1

99

Average (%)
D.E.
O.D.
IT(5)
NIT(2)IT(4)NIT(2)
System Validation 1 This result is for?
a. Confirm your knowledge
b. New knowledge
System Validation 2 Can these results help identifying theprocess which need to improve on
dissatisfaction problem?

96
78

100
80

57.5
82.5

40
90

78

80

90

100

Table 27: An interview results of system validation 2
To prove the results from application whether they have slightly distorted
from life insurance knowledge or not. The interviewee's results reply to this system's
results confirms their knowledge at least 40% from other domains (non-IT)
interviewees and the most agreement by the domain expert (non-IT) 100%. However,interviewees who are domain experts, both IT and non-IT, are accepted at least 96%.
In addition, all of them can realize new knowledge from the analysis results ofcube or data mining rules such as reveal the hidden relationship among features which
may be an impact on problems or the number of word count and word sentimentscoring express the level of the relation among features better than previous
knowledge from their experience. At least 78% of a domain expert (IT) agrees in new
knowledge from this system, and the most agreement from other domains (non-IT)
90%.
Furthermore, these results can help identify the process that needs
improvement because of a high negative score from customers. It can analyze using
the system's results match on BPMN to determine each problem's related process. At
least 80% of a domain expert (non-IT) confirm and the most agreement from otherdomain interviewees (non-IT) 100%.
System Validation

4 Can Multi-dimensional sentiment cube adapt to another domain?
Domain Expert
Other Domain
IT(5)Non-IT(2) IT(4)Non-IT(2)
5
2
4
2
0
0
0
0

a. Canb. Cannot%
100
0Table 28: An interview results of system validation 3
Next, the results show 100% that this multidimensional sentiment cube concept canadapt to other domains.

Advantage:
Recommend by domain expert (IT)  this system is useful for theorganization for analyzing the volume of customers’ dissatisfaction, especially in theera of big data generation, which opinion in product or service on social media may
express in different results of the traditional way such questionnaire. This system is
one tool to make clear pictures of dissatisfaction problems and help reduce complaints
to the company site (Interviewee#1, 4). Besides, extracting sentiment from social
media is less biased because they can express without informing personal information,
so this system is suitable for the industry in the service domain, which does business100

on the intangible product and requires evaluating satisfaction from the related feature(Interviewee#3). And this system also can extract the hidden problem and give
feedback before the significant issues occur (Interviewee#2).
Recommend by domain expert (non-IT)  this system is useful because
creating innovation using technology to develop the analysis method to record in
relationship and rules, including business flow, can express a more precise image andeasy to understand. Significantly, the measurement of word count and word sentimentscore shows a high association to identify positive or negative indicators based on therelevance of data and management theory (Interviewee#6). This system can alsopresent the trend and overall pictures of current customers’ sentiment level as endusers analysis viewpoints (Interviewee#7).
Recommend by other domains (IT)  This system deploys the concept ofmachine learning to set up rules as a figure for easy explanation to understand in rulesof business (Interviewee#9). This system also makes use of a social network to be one
source to set up a company’s strategies (Interviewee#11). Also, the measurement interms of quantitative can bring to analysis in terms of statistical and cube and show
detail in descriptive so that this system can support scientific and business
management (Interviewee#8). Especially, knowledge extraction is also useful andessential (Interviewee#10).
Recommend by other domains (non-IT)  this system shows the relationshipof key information that is important to provide the reason and impact each other.
Cross analysis among features can express the analysis results in in-depth details(Interviewee#13). This system can reveal hidden problems in a real business withrelated features each other (Interviewee#12).
Disadvantage:
Recommend by domain expert (IT)  this system is under linguistics
limitations because the Thai language is a non-standard language. Language usage isfree usage, which may impact word counts and sentiment ranking measurements(Interviewee#3). One concern with this system is that the corpus gathered fromwebsites cannot prove whether the story is real (Interviewee#4). To provide morestatistical value in multidimensional sentiment cube, it is more useful for analysis(Interviewee#5). Furthermore, some interviewee has concerned about the high budget
to implement this system into a real business (Interviewee#2).
Recommend by domain expert (non-IT)  this system requires both skill incube operation and business knowledge understanding when using this system. Someinterviewee recommends revising to store data in a database to protect data lost so thissystem can work continuously (Interviewee#6).
Recommend by other domains (IT)  this system requires high skills, both IT
and business knowledge for analysis from this system (Interviewee#11). Data tracking
such as word segmentation, word sentiment scoring requires double check manuallyfrom domain experts and general users after system-generated lexicon or corpus(Interviewee#9). Last, an accusation on a competitor in the real world business may
occur by posting a fake story on websites that we cannot prove the truth(Interviewee#10).
Recommend by other domains (non-IT)  this system concerns data loss
from user-generated contents, which occur from language usage by slang words,
101

fashion words, or typos (Interviewee#13). To manage the Thai language may takemuch effort and spend high cost in real implementation (Interviewee#12).
Recommendation:
Recommend by domain expert (IT)  the real implementation should
increase the step of data source verification by examining the admin of the source ofdata before crawl data into the system (Interviewee#4). For the real usage, if design
lexicons to support more deepen down for specific problems, it can reveal more
hidden the association among features (Interviewee#2). In real usage, a statisticalmeasurement may get better analysis in this system (Interviewee#5). Besides, to adapt
the other knowledge more than CRM may improve analysis results (Interviewee#11).
Recommend by domain expert (non-IT)  It should keep data as a revision tocompare the change of analysis results of each period and prepare a backup data planin real implementation (Interviewee#6).
Recommend by other domains (IT)  It will be useful to generate this systemwith big sample size and collect only believable websites such as has admin tomonitor post-blogs. It will be better to prove the truth of comments posted on
websites before feeding into the system. It should also set up a recurring task to a regenerated cube with revision data to compare the variance of data. If the variance islow, it can prove the results more concretely (Interviewee#8).
Recommend by other domains (non-IT)  in real practice, it requires humans
to monitor real sentiment to confirm with the system tracking to reduce mistracking
from speaking a language (Interviewee#13). So recommend updating the Thai
language database (lexicon) more frequently to collect new or modern words usefulfor semantic learning (Interviewee#12).

102

List of contributors:
1. Life Insurance domain expert with IT skill
Interviewee#1
Name:
Position:
Company:
Interviewee#2
Name:
Position:
Company:
Interviewee#3
Name:
Position:
Company:
Interviewee#4
Name:
Position:
Company:
Interviewee#5
Name:
Position:
Company:

Capt. Payoon Silagul
Chief Information Officer (CIO)
Samart Corporation PCL. (2005 - 2015) & American
International Assurance (AIA) (~ 20 years)
Miss Wipa ChareonkitsupatBusiness Unit Manager (Agency) and Financial Advisor
American International Assurance (AIA)Miss Woranuch Thanadirake
Domain Expert in Agent and Agency analysis and CRM
American International Assurance (AIA)Mr. Jaturong Soosuk
Senior solution analysis - Management Information System (IT)American International Assurance (AIA)Miss Rattanavadee Athinantaphan
Business Analyst consultant
Thai Cardif Life Insurance, a joint venture company between Thai Life
Insurance and Thai Cardif group

2. Life Insurance domain expert with non-IT
Interviewee#6
Name:
Position:
Company:
Interviewee#7
Name:
Position:
Company:Miss Suvilai
American International Assurance (AIA)Miss Vantanee Srisakundacharuk
Assistant Manager of Customer Service Center
American International Assurance (AIA)

3. Other domains with IT skill
Interviewee#8
Name:
Position:
Company:
Interviewee#9
Name:
Position:
Company:
Interviewee#10
Name:

Mr. Anan Derochanawong
AdvisorAlliance for Supporting Industries Association (A.S.I.A.)
Mr.Jakkapong Jairuk
Assistant Manager, Business Support System Division
Total Access Communication (DTAC)
Mr.Naratip Jamrus

103

Position:
Company:
Interviewee#11
Name:
Position:
Company:

Hospital Information System Module Add On Software
Software house
Mr.Suzuki Takeyuki
Phd.Student
Japan Advance Science Institute and Technology

4. Other domains with non-IT
Interviewee#12
Name:
Position:
Company:
Interviewee#13
Name:
Position:
Company:

Mr.Vichai Janjariyakun
Vice President of Operation Division
C.P. All Public company
Mr.Jaturong Kerdrat
Managing Director
Getsmart Service

104Chapter 5
Conclusions and Recommendations
5.1 Multi-dimensional sentiment cube Comparison with previous works
Many previous works such as M. Castellanos, U. Dayal, C. Gupta, S. Wang,
and M. G. Solaco (2012) also introduce the concept of Cubes and hierarchicalcorrelations for analyzing social media data. Multidimensional sentiment cube is
closely related to a conceptual modeling framework to define the new requirements
set for extracted data from social media using sentiment score associated with theseconcepts. It mainly presents in terms of a theoretical concept.
However, in this task, the development of the multidimensional sentimentcube is in term of real-world application to study how to adapt the theory of social
customer relationship management (social CRM) and Thai life insurance knowledge
base to design the semantic hierarchical of association between aspects and subaspects including their measurements. Some of the other researchers are interested ina hierarchical structure in sentiment analysis, but they did not experiment on
multidimensional cube (item 12, 17). Besides, some papers show multidimensional in
unstructured data such as documents data extraction, but they are not related tosentiment analysis (item 11, 14, 15). In term of sentiment analysis, even if many
researchers experiment with sentiment analysis widely, however, they (Table 29)
presented sentiment analysis concept in term of word-level (item 3, 13), sentencelevel (item 3, 13), and aspect-based sentiment analysis (item 3, 8, 9, 10, 12, 16, 18).
However, our task presents in terms of a multidimensional cube. Some of them havethe primary purpose of increasing sentiment classification accuracy (item 8, 10, 17,18). Furthermore, many previous works have high intentions in a standard language
such as English (item 1-17). This paper utilizes the concept of text mining and natural
language processing (NLP) to overcome the obstacle in unique identities(characteristics) of non-standard language such as the Thai language. Additionally,
the other previous works in the life insurance domain related to technology arepresented in terms of data mining technologies widely; however, it still does not findany papers directly associated with multidimensional sentiment cube.
As a part of knowledge management, the multidimensional sentiment cube
provides knowledge analysis to reveal the significant issues that directly impact
customers’ satisfaction in the Thai life insurance domain. In term of social CRM, thisresults extraction is a kind of outside-in information for engagement oriented
procedure. Then it turns the concept from one-way communication (passive CRM
strategy) to be dual-way communication (active CRM strategy) to increase theefficiency of customer acquisition, customer intention, and customer engagement
strategy.

105

Table 29: System comparison with previous works
In sum, this research presents a novel method for knowledge extraction by
extracting problems from the service issues derived from the relationship among
factors under the social CRM concept. It uses unstructured data from user-generated
content in social media and then analysis sentiment to identify problems for support106

service industrial such life insurance in the social media era. And the system also
gives an example of the process to identify the business process which requires
improving due to get high negative sentiment from a customer using event logs from a
cube.
5.2 Conclusion and Recommendations of Multidimensional Sentiment Cube
The dimensions and measurement design is important for the analysis
efficiency of multidimensional sentiment cube. The design is deeply details of aspects
and sub-aspects to present in the hierarchical of cube dimensions related to businessprocess function gains more benefits in the real analysis. Multidimensional sentimentcube can express the hidden issues from the association among multi-aspects, multisub-aspects on their measurements. And cube properties also can handle the missing
values tracking on a hierarchical structure in a more upper level of child and parent
level.
Nevertheless, this system's drawback is time-consuming in lexicon preparation
to present high accuracy in the semantic meaning on the process of text mining and
NLP. Even if this system utilizes Sansarn tagging tools to develop the specific domain
lexicon, it still requires more knowledge and technology to improve lexicon
construction with high accuracy on semantic expression automatically. It helps toreduce the developing time of this system. Another useful recommendation from theinterview process that gets from domain experts about the trustworthiness of data
sources is that we utilize data from social media such as critiqued weblogging.
Crawling the main corpus from trustable websites is highly recommended to gain
more reliable results that extract from a multidimensional sentiment cube.
5.3 Conclusion and Recommendations of an Analytic framework of Customer
Sentiment
We present the results from an analytical framework, which shows how social
media can be used as a beneficial means for problem-solving, using the concept ofsocial CRM. As such, we extracted critical words from the web blog pages to
explore customer dissatisfaction with service factors expressed through negative
feedback.
This study of Thai life insurance identified service dissatisfaction issues
extracted from social media via the sentiment extraction tool, which were thenevaluated by responses to our questionnaire. The questionnaire results provided
more details, as it helped rank the problems and their possible causes according to
the classifications of the different respondent qualifications. Moreover, thesentiment extraction tool can be useful for automatic tracking. As a real-timesystem, it could be helpful in detecting problems promptly, as they are posted.
However, the system’s effectiveness depends on its ability to learn Thai andinterpret it accurately.
It is crucial to improve the ethical knowledge of agents for increasing
customer satisfaction in Thai life insurance service and their tracking and follow up
of long-term service contracts. Such efforts will provide consistent attention topolicyholders and will help increase customer satisfaction. The claim system should107

consider adjustments to reduce prepaid medical expenses and return compensation
faster.
As most policy cancellations are due to a policyholder's change in financial
status, suggestions include establishing a long-term policy support plan, whichhelps in premium payment adjustment or the possibility of changing plans. Most
misunderstandings arise from the policyholder and the agents' complex conditions,which both the policyholder and the agents find difficult to understand; suggestions
include focusing on better education for agents and customers and providing more
information with the contract and more excellent honesty regarding the conditions.
The respondents with negative life insurance attitudes often held opinions
that differed from others, such as needing the money before its maturity date, as
their most important policy cancellation motive. The implication may be that they
prefer to spend their money rather than receive the sum assured at the end of thepolicy. They also gave a high score to agents not taking good care of customers as amajor reason leading to misunderstandings.
One limitation of this study is the use of Google documents for ourquestionnaire distribution, thereby limiting the respondents to internet users andmobile communications. In addition, many respondents resided in large cities inThailand. For more varied opinions, it is necessary to expand the respondent's sample
to various careers and areas, including those without access to the internet. In futurestudies, we will enhance our sentiment extraction tool's capacity by increasing theunderlying knowledge base through lexicon expansion and attempt to collectinformation from more diverse respondents.

108

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Appendices116Appendix A
Multidimensional Sentiment Cube Evaluation Questionnaire List
Interviewee#1 name: Capt. Payoon Silagul(Domain Expert with IT skill)
Position:
Chief Information Officer (CIO)
Company:
Samart Corporation PCL. (2005 - 2015) & American International Assurance (AIA) (~ 20 years)
Contact:
payoongsak@gmail.com
1

Does multidimensional sentiment cube or sentiment analysis have already implemented in your organization?
a.
not implement
 b.
under study
 c.
never heart before

2

In your opinion, who is proper to use this system?
 a.
domain expert
 a1.
 a2.
 a3.
 a4.
 b.
Management
 c.
Strategic plannerd.
User in organizatione.
General User

Insurance
CRM
Business Analysis
IT

System Design:
3
Do you think the concept of sentiment analysis can help identifying the customers' dissatisfaction from social media for CRM?
 Yes
100%
80%
50%
30%
0%
4

Do you think CRM concept can help identifying aspect for design?
 Yes
100%
80%
50%
30%
0%

5

Do you think process flow can help identifying weak points in current process?
 Yes
100%
80%
50%
30%
0%

6

Do you agree with Aspect design in this experiment?
 Yes
100%
80%
50%
30%

7

8

0%

Do you agree with measurement in this experiment by using word cnt and sentiment scoring?
 Yes
100%
80%
50%
30%
0%
Do you have any recommendations?
Do not have

System Usage:
9
Can the results of multidimensional sentiment cube express the relation among features of the dissatisfaction which get from social media?
 Yes
100%
80%
50%
30%
0%
10

11

12

Can the results of data mining rule express the dissatisfaction which get from social media more concretely?
 Yes
100%
80%
50%
30%
0%
Which measurement do you prefer to use for analysis ?
a.
word cnt b.
score cnt
 c.

both

Do you prefer to use multidimensional sentiment cube for analysis ?
 a.
Prefer and can analysis by yourselfb.
Prefer but cannot analysis by yourselfc.
Not prefer

116

13

14

Do you prefer to use data mining rules for setting up the dependency of severity tasks to solve problems?
a.
Prefer b.
Not prefer
What different between multidimensional sentiment cube usage and data mining rules usage in your opinion?
Multidimensional sentiment cube can answer in all of questions of end-users depend on their requirements however data mining rules can analysis trend and prediction.

System Validation:
15
This result is for?
 a.
Confirm your knowledge
 b.
New knowledge
16

17

80%
80%

100%
100%

50%
50%

30%
30%

0%
0%

Can these results help identifying the process which need to improve on dissatisfaction problem?
 Yes
100%
80%
50%
30%
0%
Can Multi-dimensional sentiment cube adapt to another domain?
a.
Can
b.
Cannot18

Advantage
This system is useful for organization for analysis the volume of customers’ dissatisfaction especially in the era of big data generation which opinion in product or service on social media may express in different results of traditional way such questionnaire. This system is one tool to make clear pictures of dissatisfaction problem and can help reduce complaint to company.

19

Disdvantage-

20

Recommendation-

Interviewee#2 name:Miss Wipa Chareonkitsupat(Domain Expert with IT skill)
Position:
Business Unit Manager (Agency) and Financial Advisor
Company:
American International Assurance (AIA)
Contact:(+6685)110-5905
1

Does multidimensional sentiment cube or sentiment analysis have already implemented in your organization?
 a.
not implementb.
under study
 c.
never heart before

2

In your opinion, who is proper to use this system?
 a.
domain expert
a1.
 a2.
a3.
a4.
 b.
Management
 c.
Strategic plannerd.
User in organizatione.
General User

Insurance
CRM
Business Analysis
IT

System Design:
3
Do you think the concept of sentiment analysis can help identifying the customers' dissatisfaction from social media for CRM?
 Yes
100% 80%
50%
30%
0%
4

Do you think CRM concept can help identifying aspect for design?
 Yes
100% 80%
50%
30%
0%

117

5

Do you think process flow can help identifying weak points in current process?
100%
 Yes
80%
50%
30%
0%

6

Do you agree with Aspect design in this experiment?
100%
 Yes
80%
50%
30%

7

8

0%

Do you agree with measurement in this experiment by using word cnt and sentiment scoring?
100%
 Yes
80%
50%
30%
0%
Do you have any recommendations?
Do not have

System Usage:
9
Can the results of multidimensional sentiment cube express the relation among features of the dissatisfaction which get from social media?
 Yes
100% 80%
50%
30%
0%
10

11

Can the results of data mining rule express the dissatisfaction which get from social media more concretely?
 Yes
100% 80%
50%
30%
0%
Which measurement do you prefer to use for analysis ?
a.
word cnt b.
score cnt
 c.

both

12

Do you prefer to use multidimensional sentiment cube for analysis ?
 a.
Prefer and can analysis by yourselfb.
Prefer but cannot analysis by yourselfc.
Not prefer13

Do you prefer to use data mining rules for setting up the dependency of severity tasks to solve problems?
 a.
Preferb.
Not prefer

14

What different between multidimensional sentiment cube usage and data mining rules usage in your opinion?
The result of multidimensional sentiment cube and data mining rules is the same, it depends on the requirements of analysis viewpoints however the statistical value is important especially negative values.

System Validation:
15
This result is for?
 a.
Confirm your knowledgeb.
New knowledge
16

100%
100%

80%
80%

50%
50%

30%
30%

0%
0%

Can these results help identifying the process which need to improve on dissatisfaction problem?
 Yes
100%
80% 50%
30%
0%

17

Can Multi-dimensional sentiment cube adapt to another domain?
a.
Can
b.
Cannot18

Advantage
This system can extract the hidden problem and give feedback before the big problems occurs.

19

Disdvantage
To implement this system may require high budget into real business.

20

Recommendation
For the real usage, if design lexicons to support more deepen down for specific problems,
it can reveal more hidden the association among features.

118

Interviewee#3 name: Miss Woranuch Thanadirake(Domain Expert with IT skill)
Position:
Domain Expert in Agent and Agency analysis and CRM
Company:
American International Assurance (AIA)
Contact:
Woranucht2@gmail.com
1

Does multidimensional sentiment cube or sentiment analysis have already implemented in your organization?
 a.
not implementb.
under studyc.
never heart before

2

In your opinion, who is proper to use this system?
 a.
domain expert
a1.
 a2.
a3.
a4.
 b.
Management
 c.
Strategic plannerd.
User in organizatione.
General User

Insurance
CRM
Business Analysis
IT

System Design:
3
Do you think the concept of sentiment analysis can help identifying the customers' dissatisfaction from social media for CRM?
 Yes
100%
80%
50%
30%
0%
4

Do you think CRM concept can help identifying aspect for design?
 Yes
100%
80%
50%
30%
0%

5

Do you think process flow can help identifying weak points in current process?
 Yes
100%
80%
50%
30%
0%

6

Do you agree with Aspect design in this experiment?
 Yes
100%
80%
50%
30%

7

8

0%

Do you agree with measurement in this experiment by using word cnt and sentiment scoring?
 Yes
100%
80%
50%
30%
0%
Do you have any recommendations?
Do not have

System Usage:
9
Can the results of multidimensional sentiment cube express the relation among features of the dissatisfaction which get from social media?
 Yes
100%
80%
50%
30%
0%
10

11

12

Can the results of data mining rule express the dissatisfaction which get from social media more concretely?
 Yes
100%
80%
50%
30%
0%
Which measurement do you prefer to use for analysis ?
a.
word cnt b.
score cnt
 c.

both

Do you prefer to use multidimensional sentiment cube for analysis ?
 a.
Prefer and can analysis by yourselfb.
Prefer but cannot analysis by yourselfc.
Not prefer

119

13

14

Do you prefer to use data mining rules for setting up the dependency of severity tasks to solve problems?
 a.
Preferb.
Not prefer
What different between multidimensional sentiment cube usage and data mining rules usage in your opinion?
Multidimensional sentiment cube present in term of relational database however data mining is for summary.

System Validation:
15
This result is for?
 a.
Confirm your knowledge
 b.
New knowledge

100%
100%

80%
80%

50%
50%

30%
30%

0%
0%

16

Can these results help identifying the process which need to improve on dissatisfaction problem?
 Yes
100%
80%
50%
30%
0%

17

Can Multi-dimensional sentiment cube adapt to another domain?
 a.
Can
b.
Cannot18

Advantage
To extract sentiment from social media is less bias because they can express without inform personal information so this system is suitable for industry in service domain which make business on intangible product and require to evaluate satisfaction from related feature.

19

Disdvantage
This system is under linguistics limitations because Thai language is non-standard language and the usage of language is freely usage which may impact on word counts and sentiment ranking measurements.

20

Recommendation-

Interviewee#4 name:Mr Jaturong Soosuk(Domain Expert with IT skill)
Position:
Senior solution analysis - Management Information System (IT)Company:
American International Assurance (AIA)
Contact:(+662) 638-6375
1

Does multidimensional sentiment cube or sentiment analysis have already implemented in your organization?
 a.
not implementb.
under studyc.
never heart before

2

In your opinion, who is proper to use this system?
 a.
domain expert
 a1.
 a2.
 a3.
a4.
 b.
Management
 c.
Strategic plannerd.
User in organizatione.
General User

Insurance
CRM
Business Analysis
IT

System Design:
3
Do you think the concept of sentiment analysis can help identifying the customers' dissatisfaction from social media for CRM?
 Yes
100% 80%
50%
30%
0%
4

Do you think CRM concept can help identifying aspect for design?
 Yes
100% 80%
50%
30%
0%

120

5

Do you think process flow can help identifying weak points in current process?
 Yes
100% 80%
50%
30%
0%

6

Do you agree with Aspect design in this experiment?
 Yes
100% 80%
50%
30%

7

8

0%

Do you agree with measurement in this experiment by using word cnt and sentiment scoring?
 Yes
100%
80% 50%30%
0%Do you have any recommendations?

System Usage:
9
Can the results of multidimensional sentiment cube express the relation among features of the dissatisfaction which get from social media?
 Yes
100% 80%
50%
30%
0%
10

11

Can the results of data mining rule express the dissatisfaction which get from social media more concretely?
 Yes
100%
80% 50%30%
0%Which measurement do you prefer to use for analysis ?
a.
word cnt b.
score cnt
 c.

both

12

Do you prefer to use multidimensional sentiment cube for analysis ?
 a.
Prefer and can analysis by yourselfb.
Prefer but cannot analysis by yourselfc.
Not prefer13

Do you prefer to use data mining rules for setting up the dependency of severity tasks to solve problems?
 a.
Preferb.
Not prefer

14

What different between multidimensional sentiment cube usage and data mining rules usage in your opinion?
Multidimensional sentiment cube can present in death analysis to serve many analysis requirements and can represent
the abstract value such satisfaction, dissatisfaction with substantial values in the same time,
However, data mining present in term of substantial values only however more concretely.

System Validation:
15
This result is for?
 a.
Confirm your knowledge
 b.
New knowledge

100%
100%

80%
80%

50%
50%

30%
30%

0%
0%

16

Can these results help identifying the process which need to improve on dissatisfaction problem?
 Yes
100% 80%
50%
30%
0%

17

Can Multi-dimensional sentiment cube adapt to another domain?
 a.
Can
b.
Cannot18

Advantage
This system is useful for organization for analysis the volume of customers’ dissatisfaction.

19

Disdvantage
The corpus which was gathered from websites cannot prove whether the story is real.

20

Recommendation
In the real implementation should increase the step of data source verification by examine in admin of source of data before crawl data into system.

121

Interviewee#5 name: Miss Rattanavadee Athinantaphan(Domain Expert with IT skill)
Position:
Business Analyst consultant
Company:
Thai Cardif Life Insurance, a joint venture company between Thai Life Insurance and Thai Cardif group
Contact:(+6686) 327-9757
1

Does multidimensional sentiment cube or sentiment analysis have already implemented in your organization?
 a.
not implementb.
under study
 c.
never heart before

2

In your opinion, who is proper to use this system?
 a.
domain expert
 a1.
 a2.
 a3.
 a4.
 b.
Management
 c.
Strategic plannerd.
User in organizatione.
General User

Insurance
CRM
Business Analysis
IT

System Design:
3
Do you think the concept of sentiment analysis can help identifying the customers' dissatisfaction from social media for CRM?
 Yes
100%
80% 50%30%
0%4Do you think CRM concept can help identifying aspect for design?
 Yes
100%
80%
50%
30%
0%

5

Do you think process flow can help identifying weak points in current process?
 Yes
100%
80%
50%
30%
0%

6

Do you agree with Aspect design in this experiment?
 Yes
100%
80%
50%
30%

7

8

0%

Do you agree with measurement in this experiment by using word cnt and sentiment scoring?
 Yes
100%
80%
50%
30%
0%
Do you have any recommendations?
Recommend to calculate about the sentiment scoring variance in statistical term with sentiment word scoring.

System Usage:
9
Can the results of multidimensional sentiment cube express the relation among features of the dissatisfaction which get from social media?
 Yes
100%
80%
50%
30%
0%
10

11

12

Can the results of data mining rule express the dissatisfaction which get from social media more concretely?
 Yes
100%
80%
50%
30%
0%
Which measurement do you prefer to use for analysis ?
a.
word cnt b.
score cnt
 c.

both

Do you prefer to use multidimensional sentiment cube for analysis ?
 a.
Prefer and can analysis by yourselfb.
Prefer but cannot analysis by yourselfc.
Not prefer

122

13

14

Do you prefer to use data mining rules for setting up the dependency of severity tasks to solve problems?
 a.
Preferb.
Not prefer
What different between multidimensional sentiment cube usage and data mining rules usage in your opinion?
Multidimensional sentiment cube is easy for analysis in multi-viewpoints, it shows in term of relationship of features so it is quite easy for understanding and suitable for represent the results of data, data mining rules is very specific however it can inform the significant of information.

System Validation:
15
This result is for?
 a.
Confirm your knowledge
 b.
New knowledge

100%
100%

80%
80%

50%
50%

30%
30%

0%
0%

16

Can these results help identifying the process which need to improve on dissatisfaction problem?
 Yes
100%
80%
50%
30%
0%

17

Can Multi-dimensional sentiment cube adapt to another domain?
 a.
Can
b.
Cannot18

Advantage
This system show the trend of customers' sentiment in multi-feature association.

19

Disdvantage
Providing more statistical value in multidimensional sentiment cube make more useful for analysis.

20

Recommendation
In the real usage, to provide the statistical measurement may get better analysis in this system.

Interviewee#6 name:Miss Suvilai Pangkavibul(Domain Expert with non-IT)
Position:
Customer Service Center Manager
Company:
American International Assurance (AIA)
Contact:
Suvilai Pangkavibul@aia.co.th (+882) 638-7556
1

Does multidimensional sentiment cube or sentiment analysis have already implemented in your organization?
 a.
not implementb.
under studyc.
never heart before

2

In your opinion, who is proper to use this system?
 a.
domain expert
 a1.
 a2.
 a3.
 a4.
 b.
Management
 c.
Strategic planner
 d.
User in organization
 e.
General User

Insurance
CRM
Business Analysis
IT

System Design:
3
Do you think the concept of sentiment analysis can help identifying the customers' dissatisfaction from social media for CRM?
 Yes
100% 80%
50%
30%
0%
4

Do you think CRM concept can help identifying aspect for design?
100%
 Yes
80%
50%
30%
0%

123

5

Do you think process flow can help identifying weak points in current process?
100%
 Yes
80%
50%
30%
0%

6

Do you agree with Aspect design in this experiment?
 Yes
100% 80%
50%
30%

7

8

0%

Do you agree with measurement in this experiment by using word cnt and sentiment scoring?
100%
 Yes
80%
50%
30%
0%
Do you have any recommendations?
Do not have

System Usage:
9
Can the results of multidimensional sentiment cube express the relation among features of the dissatisfaction which get from social media?
100%
 Yes
80%
50%
30%
0%
10

11

Can the results of data mining rule express the dissatisfaction which get from social media more concretely?
100%
 Yes
80%
50%
30%
0%
Which measurement do you prefer to use for analysis ?
a.
word cnt b.
score cnt
 c.

both

12

Do you prefer to use multidimensional sentiment cube for analysis ?
 a.
Prefer and can analysis by yourselfb.
Prefer but cannot analysis by yourselfc.
Not prefer13

Do you prefer to use data mining rules for setting up the dependency of severity tasks to solve problems?
 a.
Preferb.
Not prefer

14

What different between multidimensional sentiment cube usage and data mining rules usage in your opinion?
Multidimensional sentiment cube show relation between features which answer the reason why that problems are occurred, more flexible in analysis term and meet requirement of analysis ways as end-user want.
Data mining rules also provide trend and concrete rules in specific analysis.

System Validation:
15
This result is for?
 a.
Confirm your knowledge
 b.
New knowledge

100%
100%

80%
80%

50%
50%

30%
30%

0%
0%

16

Can these results help identifying the process which need to improve on dissatisfaction problem?
 Yes
100%
80%
50%
30%
0%

17

Can Multi-dimensional sentiment cube adapt to another domain?
 a.
Can
b.
Cannot18

Advantage
This system is useful because creating new innovation using technology for developing the analysis method to record in relationship and rules including business flow which can express more clearly image and easily to understand especially, the measurement of word count and word sentiment score show highly association to identify positive or negative indicator based on the relevant of data and management theory.

19

Disdvantage
This system requires both skill in cube operation and business knowledge understanding when use this system.

20

Recommendation
Recommends make revision to store data in database in order to protect data lost so this system can work continuously.
It should keep data as revision to compare change of analysis results of each period and should prepare back up data plan in real implementation.

124

Interviewee#7 name: Miss Vantanee Srisakundacharuk(Domain Expert with non-IT)
Position:
Assistant Manager of Customer Service Center
Company:
American International Assurance (AIA)
Contact:(+6685) 022-2512
1

Does multidimensional sentiment cube or sentiment analysis have already implemented in your organization?
 a.
not implementb.
under studyc.
never heart before

2

In your opinion, who is proper to use this system?
 a.
domain expert
 a1.
 a2.
 a3.
 a4.
 b.
Management
 c.
Strategic planner
 d.
User in organization
 e.
General User

Insurance
CRM
Business Analysis
IT

System Design:
3
Do you think the concept of sentiment analysis can help identifying the customers' dissatisfaction from social media for CRM?
 Yes
100%
80%
50%
30%
0%
4

Do you think CRM concept can help identifying aspect for design?
 Yes
100%
80%
50%
30%
0%

5

Do you think process flow can help identifying weak points in current process?
 Yes
100%
80% 50%30%
0%

6Do you agree with Aspect design in this experiment?
 Yes
100%
80%
50%
30%

7

8

0%

Do you agree with measurement in this experiment by using word cnt and sentiment scoring?
 Yes
100%
80%
50%
30%
0%
Do you have any recommendations?
Do not have

System Usage:
9
Can the results of multidimensional sentiment cube express the relation among features of the dissatisfaction which get from social media?
 Yes
100%
80%
50%
30%
0%
10

11

12

Can the results of data mining rule express the dissatisfaction which get from social media more concretely?
 Yes
100%
80% 50%30%
0%Which measurement do you prefer to use for analysis ?
a.
word cnt b.
score cnt
 c.

both

Do you prefer to use multidimensional sentiment cube for analysis ?
 a.
Prefer and can analysis by yourselfb.
Prefer but cannot analysis by yourselfc.
Not prefer

125

13

14

Do you prefer to use data mining rules for setting up the dependency of severity tasks to solve problems?
 a.
Preferb.
Not prefer
What different between multidimensional sentiment cube usage and data mining rules usage in your opinion?
I prefer multidimensional sentiment cube because it show figures in all viewpoints which easy to understand and comparable however data mining rules is a data conclusion in feeling of end-users.

System Validation:
15
This result is for?
 a.
Confirm your knowledge
 b.
New knowledge

100%
100%

80%
80%

50%
50%

30%
30%

0%
0%

16

Can these results help identifying the process which need to improve on dissatisfaction problem?
 Yes
100%
80%
50%
30%
0%

17

Can Multi-dimensional sentiment cube adapt to another domain?
 a.
Can
b.
Cannot18

Advantage
This system can present the trend and overall pictures of currently of customers’ sentiment level as end-users analysis viewpoints.

19

Disdvantage-

20

Recommendation-

Interviewee#8 name:Mr. Anan Derochanawong(Other Domain with IT skill)
Position:
AdvisorCompany:
Alliance for Supporting Industries Association (A.S.I.A.)
Contact:(+662) 712-1729
1

Does multidimensional sentiment cube or sentiment analysis have already implemented in your organization?
 a.
not implementb.
under studyc.
never heart before

2

In your opinion, who is proper to use this system?
 a.
domain expert
 a1.
 a2.
a3.
a4.
b.
Managementc.
Strategic plannerd.
User in organizatione.
General User

Insurance
CRM
Business Analysis
IT

System Design:
3
Do you think the concept of sentiment analysis can help identifying the customers' dissatisfaction from social media for CRM?
 Yes
100%
80% 50%30%
0%4Do you think CRM concept can help identifying aspect for design?
 Yes
100%
80% 50%
30%
0%

126

5

Do you think process flow can help identifying weak points in current process?
 Yes
100% 80%
50%
30%
0%

6

Do you agree with Aspect design in this experiment?
100%
 Yes
80%
50%
30%

7

8

0%

Do you agree with measurement in this experiment by using word cnt and sentiment scoring?
100%
 Yes
80%
50%
30%
0%
Do you have any recommendations?
Do not have

System Usage:
9
Can the results of multidimensional sentiment cube express the relation among features of the dissatisfaction which get from social media?
100%
 Yes
80%
50%
30%
0%
10

11

Can the results of data mining rule express the dissatisfaction which get from social media more concretely?
100%
 Yes
80%
50%
30%
0%
Which measurement do you prefer to use for analysis ?
a.
word cnt b.
score cnt
 c.

both

12

Do you prefer to use multidimensional sentiment cube for analysis ?
 a.
Prefer and can analysis by yourselfb.
Prefer but cannot analysis by yourselfc.
Not prefer13

Do you prefer to use data mining rules for setting up the dependency of severity tasks to solve problems?
 a.
Preferb.
Not prefer

14

What different between multidimensional sentiment cube usage and data mining rules usage in your opinion?
Multidimensional sentiment cube show death analysis viewpoints and data mining rules show the significant of data so I prefer to use both of information.

System Validation:
15
This result is for?
 a.
Confirm your knowledge
 b.
New knowledge
16

17

100%
100%

80%
80%

50%
50%

30%
30%

0%
0%

Can these results help identifying the process which need to improve on dissatisfaction problem?
100%
 Yes
80%
50%
30%
0%
Can Multi-dimensional sentiment cube adapt to another domain?
 a.
b.

Can
Cannot18

Advantage
The measurement in term of quantitative can bring to analysis in term of statistical and cube also show detail in descriptive so this system can support both of scientific and business management.

19

Disdvantage-

20

Recommendation
It will be good if generate this system with big sample size and collect only the believable websites such has admin to monitor post-blogs and it will be better to prove the truth of comments which are posted in websites before feed into system. In addition, it should set up recurring task to re-generated cube with revision data in order to compare the variance of data. If the variance is less, it can prove the results more concretely.

127Interviewee#9 name: Mr.Jakkapong Jairak(Other Domains with IT skill)
Position:
Assistant Manager, Business Support System Division
Company:
Total Access Communication (DTAC)
Contact:
jakkapong.jairak@dtac.co.th
1

Does multidimensional sentiment cube or sentiment analysis have already implemented in your organization?
 a.
not implementb.
under studyc.
never heart before

2

In your opinion, who is proper to use this system?
 a.
domain expert
a1.
a2.
 a3.
 a4.
 b.
Management
 c.
Strategic plannerd.
User in organizatione.
General User

Insurance
CRM
Business Analysis
IT

System Design:
3
Do you think the concept of sentiment analysis can help identifying the customers' dissatisfaction from social media for CRM?
 Yes
100%
80%
50%
30%
0%
4

Do you think CRM concept can help identifying aspect for design?
 Yes
100%
80%
50%
30%
0%

5

Do you think process flow can help identifying weak points in current process?
 Yes
100%
80%
50%
30%
0%

6

Do you agree with Aspect design in this experiment?
 Yes
100%
80%
50%
30%

0%

7

Do you agree with measurement in this experiment by using word cnt and sentiment scoring?
 Yes
100%
80% 50%30%
0%8Do you have any recommendations?
Recommend to use emoticon or stickers for sentiment measurement include into this system

System Usage:
9
Can the results of multidimensional sentiment cube express the relation among features of the dissatisfaction which get from social media?
 Yes
100%
80% 50%
30%
0%
10

11

12

Can the results of data mining rule express the dissatisfaction which get from social media more concretely?
 Yes
100%
80% 50%30%
0%Which measurement do you prefer to use for analysis ?
a.
word cnt b.
score cnt
 c.

both

Do you prefer to use multidimensional sentiment cube for analysis ?
 a.
Prefer and can analysis by yourselfb.
Prefer but cannot analysis by yourselfc.
Not prefer

128

13

14

Do you prefer to use data mining rules for setting up the dependency of severity tasks to solve problems?
 a.
Preferb.
Not prefer
What different between multidimensional sentiment cube usage and data mining rules usage in your opinion?
Multidimensional sentiment cube use for analysis in many viewpoints of analysis however data mining rules use in specific analysis.

System Validation:
15
This result is for?
 a.
Confirm your knowledge
 b.
New knowledge

100%
100%

80%
80%

50%
50%

30%
30%

0%
0%

16

Can these results help identifying the process which need to improve on dissatisfaction problem?
 Yes
100%
80%
50%
30%
0%

17

Can Multi-dimensional sentiment cube adapt to another domain?
 a.
Can
b.
Cannot18

Advantage
This system deploy the concept of machine learning to set up rules as a figure for easily explanation to understand in rules of business.

19

Disdvantage
Data tracking such as word segmentation, word sentiment scoring requires double check manually from domain experts and general users after system generated lexicon or corpus.

20

Recommendation-

Interviewee#10 name:Mr.Naratip Jamrus(Other Domains with IT skill)
Position:
Hospital Information System Module Add On Software
Company:
Software house
Contact:(+6693) 183-9617
1

Does multidimensional sentiment cube or sentiment analysis have already implemented in your organization?
 a.
not implementb.
under studyc.
never heart before

2

In your opinion, who is proper to use this system?
 a.
domain expert
a1.
a2.
 a3.
a4.
 b.
Management
 c.
Strategic plannerd.
User in organizatione.
General User

Insurance
CRM
Business Analysis
IT

System Design:
3
Do you think the concept of sentiment analysis can help identifying the customers' dissatisfaction from social media for CRM?
 Yes
100%
80%
50%
30%
0%
4

Do you think CRM concept can help identifying aspect for design?
 Yes
100%
80%
50%
30%
0%

129

5

Do you think process flow can help identifying weak points in current process?
 Yes
100%
80%
50%
30%
0%

6

Do you agree with Aspect design in this experiment?
 Yes
100%
80%
50%
30%

7

8

0%

Do you agree with measurement in this experiment by using word cnt and sentiment scoring?
 Yes
100%
80%
50%
30%
0%
Do you have any recommendations?
Do not have

System Usage:
9
Can the results of multidimensional sentiment cube express the relation among features of the dissatisfaction which get from social media?
 Yes
100%
80%
50%
30%
0%
10

11

Can the results of data mining rule express the dissatisfaction which get from social media more concretely?
 Yes
100% 80%
50%
30%
0%
Which measurement do you prefer to use for analysis ?
a.
word cnt b.
score cnt
 c.

both

12

Do you prefer to use multidimensional sentiment cube for analysis ?
 a.
Prefer and can analysis by yourselfb.
Prefer but cannot analysis by yourselfc.
Not prefer13

Do you prefer to use data mining rules for setting up the dependency of severity tasks to solve problems?
 a.
Preferb.
Not prefer

14

What different between multidimensional sentiment cube usage and data mining rules usage in your opinion?
-

System Validation:
15
This result is for?
 a.
Confirm your knowledge
 b.
New knowledge

100%
100%

80%
80%

50%
50%

30%
30%

0%
0%

16

Can these results help identifying the process which need to improve on dissatisfaction problem?
 Yes
100%
80%
50%
30%
0%

17

Can Multi-dimensional sentiment cube adapt to another domain?
 a.
Can
b.
Cannot18

Advantage
Knowledge extraction which get from system is useful and important.

19

Disdvantage
An accusation on competitor in the real world business may occur by post the fake story into websites which we cannot prove the truth.

20

Recommendation-

130Interviewee#11 name: Mr.Suzuki Takeyuki(Other Domain with IT skill)
Position:
Phd.Student
Company:
Japan Advance Science Institute and Technology
Contact:t_suzuki@jaist.ac.jp
1

Does multidimensional sentiment cube or sentiment analysis have already implemented in your organization?
a.
not implement
 b.
under studyc.
never heart before

2

In your opinion, who is proper to use this system?
 a.
domain expert
 a1.
 a2.
 a3.
a4.
 b.
Management
 c.
Strategic plannerd.
User in organizatione.
General User

Insurance
CRM
Business Analysis
IT

System Design:
3
Do you think the concept of sentiment analysis can help identifying the customers' dissatisfaction from social media for CRM?
 Yes
100%
80%
50%
30%
0%
4

Do you think CRM concept can help identifying aspect for design?
 Yes
100%
80% 50%30%
0%5Do you think process flow can help identifying weak points in current process?
 Yes
100%
80%
50%
30%
0%

6

Do you agree with Aspect design in this experiment?
 Yes
100%
80%
50%
30%

7

8

0%

Do you agree with measurement in this experiment by using word cnt and sentiment scoring?
 Yes
100%
80%
50%
30%
0%
Do you have any recommendations?
Do not have

System Usage:
9
Can the results of multidimensional sentiment cube express the relation among features of the dissatisfaction which get from social media?
 Yes
100%
80%
50%
30%
0%
10

11

12

Can the results of data mining rule express the dissatisfaction which get from social media more concretely?
 Yes
100%
80%
50%
30%
0%
Which measurement do you prefer to use for analysis ?
a.
word cnt b.
score cnt
 c.

both

Do you prefer to use multidimensional sentiment cube for analysis ?
 a.
Prefer and can analysis by yourselfb.
Prefer but cannot analysis by yourselfc.
Not prefer

131

13

14

Do you prefer to use data mining rules for setting up the dependency of severity tasks to solve problems?
 a.
Preferb.
Not prefer
What different between multidimensional sentiment cube usage and data mining rules usage in your opinion?
Multidimensional sentiment cube use for analysis group or categorize, data mining(association rule) can know relationship of each aspect with their direction.

System Validation:
15
This result is for?
a.
Confirm your knowledge
 b.
New knowledge

100%
100%

80%
80%

50%
50%

30%
30%

0%
0%

16

Can these results help identifying the process which need to improve on dissatisfaction problem?
 Yes
100%
80%
50%
30%
0%

17

Can Multi-dimensional sentiment cube adapt to another domain?
 a.
Can
b.
Cannot18

Advantage
This system also makes use of social network to be one source to set up company’s strategies.

19

Disdvantage
This system requires highly skill both of IT and business knowledge for analysis from this system.

20

Recommendation-

Interviewee#12 name:Mr.Vichai Janjariyakun
Position:
Vice President of Operation Division
Company:
C.P. All Public company
Contact:(+6691) 789-6999(Other Domains with non-IT)

1

Does multidimensional sentiment cube or sentiment analysis have already implemented in your organization?
 a.
not implementb.
under studyc.
never heart before

2

In your opinion, who is proper to use this system?
 a.
domain expert
 a1.
 a2.
 a3.
 a4.
 b.
Management
 c.
Strategic plannerd.
User in organizatione.
General User

Insurance
CRM
Business Analysis
IT

System Design:
3
Do you think the concept of sentiment analysis can help identifying the customers' dissatisfaction from social media for CRM?
100%
 Yes
80%
50%
30%
0%
4

Do you think CRM concept can help identifying aspect for design?
100%
 Yes
80%
50%
30%
0%

132

5

Do you think process flow can help identifying weak points in current process?
100%
 Yes
80%
50%
30%
0%

6

Do you agree with Aspect design in this experiment?
100%
 Yes
80%
50%
30%

7

8

0%

Do you agree with measurement in this experiment by using word cnt and sentiment scoring?
100%
 Yes
80%
50%
30%
0%
Do you have any recommendations?
Do not have

System Usage:
9
Can the results of multidimensional sentiment cube express the relation among features of the dissatisfaction which get from social media?
100%
 Yes
80%
50%
30%
0%
10

11

Can the results of data mining rule express the dissatisfaction which get from social media more concretely?
100%
 Yes
80%
50%
30%
0%
Which measurement do you prefer to use for analysis ?
a.
word cnt b.
score cnt
 c.

both

12

Do you prefer to use multidimensional sentiment cube for analysis ?
 a.
Prefer and can analysis by yourselfb.
Prefer but cannot analysis by yourselfc.
Not prefer13

Do you prefer to use data mining rules for setting up the dependency of severity tasks to solve problems?
 a.
Preferb.
Not prefer

14

What different between multidimensional sentiment cube usage and data mining rules usage in your opinion?
-

System Validation:
15

This result is for?
 a.
Confirm your knowledge
 b.
New knowledge

100%
100%

80%
80%

50%
50%

30%
30%

0%
0%

16

Can these results help identifying the process which need to improve on dissatisfaction problem?
 Yes
100%
80%
50%
30%
0%

17

Can Multi-dimensional sentiment cube adapt to another domain?
 a.
Can
b.
Cannot18

Advantage
This system can reveal hidden problems in real business with related features each other.

19

Disdvantage
To manage Thai language may take a lot effort and spend high cost in real implementation.

20

Recommendation
To update Thai language database (lexicon) more frequency to collect new or modern words.

133

Interviewee#13 name: Mr.Jaturong Kerdrat
Position:Managing Director
Company:
Getsmart Service
Contact:(+6683) 555-5391(Other Domains with non-IT)

1

Does multidimensional sentiment cube or sentiment analysis have already implemented in your organization?
 a.
not implementb.
under studyc.
never heart before

2

In your opinion, who is proper to use this system?
 a.
domain expert
 a1.
 a2.
 a3.
a4.
 b.
Management
 c.
Strategic plannerd.
User in organization
 e.
General User

Insurance
CRM
Business Analysis
IT

System Design:
3
Do you think the concept of sentiment analysis can help identifying the customers' dissatisfaction from social media for CRM?
 Yes
100%
80%
50%
30%
0%
4

Do you think CRM concept can help identifying aspect for design?
 Yes
100%
80%
50%
30%
0%

5

Do you think process flow can help identifying weak points in current process?
 Yes
100%
80%
50%
30%
0%

6

Do you agree with Aspect design in this experiment?
 Yes
100%
80%
50%
30%

7

8

0%

Do you agree with measurement in this experiment by using word cnt and sentiment scoring?
 Yes
100%
80%
50%
30%
0%
Do you have any recommendations?
Do not have

System Usage:
9
Can the results of multidimensional sentiment cube express the relation among features of the dissatisfaction which get from social media?
 Yes
100%
80%
50%
30%
0%
10

11

12

Can the results of data mining rule express the dissatisfaction which get from social media more concretely?
 Yes
100%
80%
50%
30%
0%
Which measurement do you prefer to use for analysis ?
a.
word cnt b.
score cnt
 c.

both

Do you prefer to use multidimensional sentiment cube for analysis ?
 a.
Prefer and can analysis by yourselfb.
Prefer but cannot analysis by yourselfc.
Not prefer

134

13

14

Do you prefer to use data mining rules for setting up the dependency of severity tasks to solve problems?
 a.
Preferb.
Not prefer
What different between multidimensional sentiment cube usage and data mining rules usage in your opinion?
-

System Validation:
15
This result is for?
 a.
Confirm your knowledge
 b.
New knowledge

100%
100%

80%
80%

50%
50%

30%
30%

0%
0%

16

Can these results help identifying the process which need to improve on dissatisfaction problem?
 Yes
100%
80%
50%
30%
0%

17

Can Multi-dimensional sentiment cube adapt to another domain?
 a.
Can
b.
Cannot18

Advantage
This system show the relationship of key information that is important to provide the reason and impact each other.
Cross analysis among features can express the analysis results in depth details.

19

Disdvantage
This system concerns on loss data from user-generated contents which occur from language usage by slang words, fashion words or typo.

20

Recommendation
In the real practice, it requires human to monitor real sentiment to confirm with system tracking in order to reduce mis-tracking from speaking language.

135

Appendix B

Survey on satisfaction of life insurance services in Thailand

Respondents Profile:
1. Gendera. Maleb. Femalec. unspecified

a. Single

b. Marriagec. Widowa. Undergraduate

b. Bachelor's
Degreec. Master's Degreed. Doctor's Degree

e. Diploma or
Certificate

2. Age___________ years old
3. Status
4. Monthly Income ___________baht
5. Occupation ___________
6. Education level7.

Do you have work experience or
study in life insurance?

a. Yes b. No

136

Life Insurance Characteristic of respondents (If you do not have a life insurance policy. Answer only
items 1 and 2 and skip to the next section on 'Life insurance attitude' without answering questions 3-10 in this topic.)

1.

Do you have life insurance?

2.

Do you decide to buy life insurance for your family?

a. Yesb. No

3.

Life Insurance company of your policya. AIAb. Thai Life Insurancec. Alianze Life
Insuranced. SCB Life Assurance

e. Bangkok Life Insurance f. Muang Thai Life Assurance g. Others________

Type of Life Insurance which you holda. Endowment

b. Whole Lifec. Termd. Retirement

e. Investment

f. Others ________

b. More than 10 years but
less than 20 years

c. 20 years up

4.

a. Have ___ policies

b. Do not have5.

Period of coveragea. Less than or equal 10
years

6.

Total of premium per year of all policies

a. Less than of equal 15,000 baht b. 15,000 – 30,000 bahtc. 30,001 – 60,000
baht

d. 60,001 – 100,000 bathe. 100,001 – 200,000 baht

f. 200,000 baht up

c. Half year

7.

How many years have you been a life
insurance customer?

….. years

8.

Payment method

a. Monthly

b. Quarterly

d. Yearlye. One-time payment

9.

Have you ever use service of life insurance company? Which service? (You can reply many answers)
 Yes, I ever use service

 Never use
servicea. General inquiry

b. Request make new life insurance policyc. Request claimd. Request make new rider such as H&S, Cancer
protection

e. Request change status

f. Make policy cancellation

137

g. Others___________

10. Which channel do you prefer to contact for getting service from life insurance company?
a. Agentb. Broker

c. Bankd. Call centere. Website

d. Government
Organization

Attitude about Life Insurance
1.

Do you think life insurance is important?

a. Very importantb. Importantc. Notimportant2.

How do you know the criticism about theservice of life insurance?

a. Experienced

b. No direct experience.
But realize from social
media

c. No direct
experience.
And do notknow anydetails3.

When you experience an unsatisfied
service, you will…

a. Negligent

b. Do not return to get
service from that company

c. Spread theword to others

4.

From your experience about service inlife insurance. What is your sentiment?

a. Positiveb. Negativec. Neutral

5.

Do you believe in criticism which you’ve
heard from social media such as
Facebook, Line, Twitter, websites fromcelebrity interviews?

b. Quite believec. Do notbelieve

6.

In your opinion, why some person do notwant to buy life insurance? (checkbox)

Feel annoyed by agents
who offer insurance plans.

Strong health

Another investment. Moreprofitable

Not sure toclaim success.

Life insurance companies are more
beneficial than provide benefits.
There is insurance in other kind on handa. Absolutely believe

Do not want to pay
because it does notnecessary.
Life insurance company
and agent cheated
No one is worried.

138

Happy with social security

Part 1. Evaluate knowledge about life insurance.
1
Less

Sentiment Level

2

3
More

4

5
Most

1
2

Do you think how much you have any knowledge about life insurance?
Before making decision to make a life insurance policy, do you think you
understand the contents and exceptions in the policy?
3 After receiving the life insurance policy, do you think you understand thecontents and exceptions in the policy?
4 Do you need more study about the contents and exceptions inside the policy than
the agent explanations under limited time or not?
5 Do you think that there are some terms or exceptions of the policy that you think
you have misunderstood?
6 Do you understand the meaning of the sum assurance, the contractual
reimbursement, and the dividend on the life insurance?
7 Do you understand about “Free look period” which is to cancel the life insurance
policy within 15 days from the date the insured received the policy?
8 Do you know? If you do not reveal your health history exactly, there is a riskthat you will not be able to claim benefits or lead to policy cancellation.
9 Do you understand? At the expiration period of the policy. Total sum assure oflife insurance policy will be fully refunded under the contract. However thedividends may vary.
10 Do you know? If you want to make policy cancellation or lapse, you can choose
to change status of them instead such as reducing the sum assure or reducing thecoverage period. This results in no premium payment. You do not have to cancel
the policy, which will lose benefits.

Part 2. Evaluate the service of policy offering, claim the policy, apply for change
status, and make policy cancellation.
1
Less

Sentiment Level
1
2
3

Do you feel that you have been tempted to make your current policy? and you
are not sure whether this policy meets your needs or not.
Do you think that the insurance agent provides distortion or concealment ofpolicy terms to customers?
Please evaluate the disqualification of life insurance service nowadays.
a. Reliability and integrity in service
139

2

3
More

4

5
Most

Product and service knowledgec. Responsibility in service to customers
d. Continuous customer service throughout the policy period
e. Personality, service etiquette
Have you heard or experienced of problems on the service process below?
a. Presentation and issuance of new policy processb. Claim process when stay at hospitalc. Claim process to receive compensationd. Policy change request process such as cancel policy or rider
e. Marketing and promotion process
f. Refund sum assured process. (when policy expires)Have you heard or experienced on any claim problems below?
a. Cannot contact agent or no service provider support
b. Do not understand in claim exceptions
c. The process of claim at the hospital is very delicate.
d. Long time to get compensatione. Get compensation return less than expectationf. Cannot claim from diseaseg. It is inconvenience to make a reservation for medical expenses inadvance.
h. Health record concealment problem
From your experience, what is the reason why customers apply for changing
policy status?
a. Request a change because of poor service
b. Request a change because the policy does not meet the requirements
c. Request a change due to demand or financial status has been changed
From your experience, what is the reason why customers apply policy
cancellation?
a. Make a cancellation due to poor service
b. Make a cancellation because the policy does not meet therequirements.
b.

4

5

6

7

c.

8

Make a cancellation due to demand or financial status has been changed

d. Make a cancellation due to want to change insurance company
e. Make a cancellation to take out insurance funds ahead of time
When you get a dissatisfaction service, you choose toa. Litigation company or agent
140

b.
c.
d.
e.

9.

10.

Post a topic in social media about the service that comes across
Spread the word to relatives and friends
Make a policy cancellation
Contact the management of the company in order to negotiate andsolve problems.
Which of the following problems occur from a misunderstanding about the
principle of life insurance between customers and service providers?
a. There are many policy conditions to understand in short termb. Service providers such as insurance agents inform customers only thebenefits of the policy which they offer for salesc. Service provider does not check the customer's understanding before
making a contract.
d. Economic situation is changed and some benefits such as dividends
may impact.
e. Lack of service providers with sufficient knowledge
Please rate the misconception issues of life insurance service nowadays.
(1 is less problem, 5 is a lot problems)a. the scope of coverageb. the conditions of life insurance coveragec. the scope of coverage - diseased. the duration of the disease

Part 3. Evaluate the service improvement.
1
Less

Sentiment Level
1.

2.

How do you give important to this service type?
a. Reliability and honesty in service
b. Product or service knowledgec. Responsibility in service to customers
d. Continuous customer service throughout the policy period
e. Personality, service etiquette
Please rate the method which is possible to solve the dissatisfaction problem (1= less help, 5 is most help)
a. Negotiations to compensate for the loss together
b. Offer an opportunity to change insurance typec. Find a central authority organization to help negotiation such as the
141

2

3
More

4

5
Most

3.

4.

5.
6.

Department of Insurance.
d. Discounts on annual premiums (In case the customer is correct)
e. Provide gifts such as free health check card
When customers want to make policy cancellation which cause to loss benefits,
what do you think about these offers of status changing as the following?
a. Reduce sum assured by average the amount of premiums havealready paid under the same protection period and customer can stop
payment premium in the next year with active policyb. Reduce period of coverage by average the amount of premiums havealready paid under the same sum assured and customer can stop
payment premium in the next year with active policy
What activities do you think will enhance your understanding of life insurance?
To reduce misunderstanding between customers and service providers in thelong run.
a. Product knowledge training to customers who hold that policy typeb. Training to provide understanding especially in highlighted problem.
c. Product knowledge and ethics training to agent
d. Send mail or message via social media to describe collateral andsimple exceptions such as infographic, short-clips, cartoonse. Make a contact customer for asking an understanding or provide any
help on some problems, especially when the policy details are changed
f. Provide a simple questionnaire to customer for self-checking an
understandingg. Make a life insurance brochure placed by general place such as
department store, BTS.
h. Organize a department to review customer opinion especiallyproblem from social media.
i. Organize troubleshooting agencies for an individual support in theurgent casej. Keep evaluate the customer satisfaction in each period
What do you think? If customer’s attitude toward life insurance is better, it canmake the growth of life insurance in Thailand?
Other recommendation for service improvement

142

List of publications and presentations:1.

Sucharittham, N., Threeramunkong, T., Haruechaiyasak, C., Bao, H. T., &Hieu-Chi Dam, Data Mining for Life Insurance Knowledge Extraction: A
Survey, The Asian Conference on Information Systems (ACIS2013), Bangkok,
Thailand, 2013.

2.

Sucharittham, N., Haruechaiyasak, C., Theeramunkong, T., & Hieu-Chi Dam,
Exploiting Linguistic Knowledge for Sentiment Classification in Online
Opinions related to Life Insurance Topic in Thailand, The Asian Conferenceon Information Systems (ACIS2014), Natrang, Vietnam, 2014.

3.

Sucharittham N., Nguyen T., Theeramunkong T., Hieu-Chi Dam, Customers’
Sentiment in Thai Life Insurance, International Journal of Management,
Business, and Economics, 2020. (Accepted, 2020), 15 pages.

4.

Sucharittham N., Nguyen T., Theeramunkong T., Hieu-Chi Dam, Customer
Sentiment Knowledge Management: A case study in Thai life insurance,
International Journal of Management, Business, and Economics, 2021.
(Accepted, 2021), 14 pages.

5.

Sucharittham, N., Haruechaiyasak, C., Hieu-Chi Dam, Threeramunkong, T.,
Multi-dimensional sentiment cube mining for process monitoring, Walailak
Journal of Science and Technology, 2020 (Under review), 20 pages.

6.

Sucharittham, N., Haruechaiyasak, C., Hieu-Chi Dam, Threeramunkong, T., A
design of multi-dimensional sentiment cube on social customer relationshipmanagement, Journal of Intelligent Information Systems, 2021 (Under
review), 25 pages.

143