Exploring Keywords to Create Tourism Markets by Using Blog Text Mining
つくば国際大学 加藤 淳一*
Tsukuba International University

Kato, Junichi

1 Introduction
Some theme parks in Japan had faced difficulties on their business. One of them is Huis Ten Bosch(HTB). HTB has gone into the red since its start. However HTB recorded to be in the first black on
June 22th 2011. Web article1 of Yomiuri Shinbun newspaper reported there was the event about
flowers and lights as the background.
At a glance, the keyword as flower is not a special word for us. We Japanese people go to see thecherry blossoms in spring. But this keyword is extraordinary for travel agents. Nikkei Information
Strategy magazine (2009, February, pp.48-49) reported that the think-tank of JTB group(Tabino-Hansoku kenkyujyo) executed focus group interviews to customers who were 20 and 50 years
old in Tokyo and Osaka four times and found out a keyword as flower. Then JTB created a
promotional campaign that name was a gorgeous Japan campaign (Hanayakana Nihon campaign)and the performance of that promotional campaign was great.
As this example, we need some keywords to create new markets. The concrete tool for think-tank of
JTB was interview. However, this method limits the amount as small numbers of opinions of people.
When managers hope to cultivate larger markets, they would like to gather lager number of opinions
and understand their opinions in detail. The text mining is one tool which makes this come true.
The object of this paper is to find some keywords for creating new tourism markets by using text
mining. I take Huis Ten Bosch (HTB) as one example in this research. I execute text mining of blogs.
In the previous works, other researchers analyzed blogs about travel by using text mining. In the next
section, I start this paper from reviewing the existing researches. I point out the possibilities ofanalyzing of travel blogs. And I clarify the contribution of this research through this review process.
2 Reviews
Some researchers focused on the blog as natural language data and analyzed them. Pan et al. (2007)
clarified the strength and weakness by categorizing that the blogs’ contents about destinations are
positive or negative.
After reviewing the previous works of Carson (2008) and Pan et al. (2007), Volo (2010) insisted thatthe existing researches simplified and operated the concept of experience into offerings and did not
examine effects on the blog readers or latent tourists (future travel intensions). So Volo (2010) focusedon the experience of consumers in the destination by using blog data
Lastly, Mitamura et al. (2008) not only discussed blog text related to tourism, but also comparedwith two categories of blogs for clarifying the characteristics of the contents of blogs. The one category
is related to tourism blogs and the other is related to general blogs. Mitamura et al. (2008) counted thefrequency of words which they called as the tourism keywords in blogs2 .
Through comparing a blog text mining with open-ended questions, I summarize the possibilities of
blogs as data on the basis of these existing researches in the following discussion. First according to* つくば国際大学産業社会学部 (Tsukuba International University)、E-mail: junichikato01@gmail.com

1 http://www.yomiuri.co.jp/atmoney/news/20110623-OYT1T00143.htm (The last date of access is on July, 4, 2011.)
2 Mitamura et al. (2008, p.59) showed three methods. They were (1) burst analysis, (2) trend analysis, and (3) behavior analysis.
However depending on the results of their paper, I cannot find out the result of behavior analysis (time of writing of blogs). Additionally,
the burst analysis was very limited share of article body. As the result, in my review section, the contribution of their research isfrequency and trend analysis.

Volo (2010, p. 308), “Compared to other research methods, blogs allow for an analysis of individual
generated content (narratives/ text, pictures and videos) and community generated content(comments present in each blog and their dynamic)”. This means that we can analyze natural
language data of both sides. It is hard to achieve this by using questionnaires and shows possibilitiesof blogs.
Second, we can use the collected blog texts that blog authors write spontaneously rather thanquestionnaires that researchers request to answer. As Volo (2010, p.308) pointed out, “…travel blogs
can be an effective, unobtrusive method of collecting data …”. This means that blog texts as data haveless possibility that force test subjects to create data.
Third, despite Pan et al. (2007) and Volo (2010) analyzed small amount of data by hands, Mitamura
et al. (2008) analyzed huge amount of natural language data like 32841and 8699 blog entries. In spiteof a massive data in web-sphere, analysis by hands limited data which we analyze to the amount ofdata which we can deal with. Mitamura et al. (2008) used computer program for analysis andprocessed the big data we cannot analyze by hands. In other words, fourth, we can get and process
massive natural language data from blogs rather than questionnaires of open-ended.
Finally, we can gather wider-angle opinions related to keywords by using the search engine fromvarious people. These previous works (Pan et al. (2007), Volo (2010) and Mitamura et al. (2008)) used a
search engine. With this method, these previous researches could collect data that we cannot get byquestionnaires from wide-range subjects.
The existing researches show the unique contributions of blogs as data. However, we still haveproblems to need to be solved. Although blogs as data have a merit that we can identify the authors ofthese massive blogs besides these contributions, even Mitamura et al. (2008) who used computer
program for analysis of the only frequency of words. He did not use this merit that blog data is
massive data and authors are identified. If we use this merit of blog data, we can cluster blog authors3
and can segment markets.
According to Kotler and Keller (2006, p.240), “A market segment consists of a group of customers
who share a similar set of needs and wants”. In this research, I categorize authors and clarify theneeds of customers. I use Kato & Ishikawa Procedure (KIP) for this aim.
This procedure is one solution for the following two problems. According to Volo (2010, p.309),“However, there are currently few standard procedures for interpreting and coding blog narratives
and comments as much needed research remains to understand all of the potential benefits and how
to manage the many problems. It presents, including the practical problem of being very time
consuming and not very amenable to rigorous sampling standards” and Pan et al. (2007, p.43) said
that“To help marketers and others monitor information on their destination, and automatedmonitoring system for online blogs could be created”.
This KIP answers the three of four possibilities of blogs we showed in the above discussion and theremaining one problem to be solved. So without first possibility (both-sided natural language data) I
pointed out, I can use massive natural language data (second possibility of blogs) which authors wrote
spontaneously (third possibility of blogs) from the blog search engine ( fourth possibility of blogs) andcluster these authors on the basis of dimensions that the existing marketing researches proposed (theremaining one problem). These are contributions of this research. But first possibility of blogs I
pointed out in above discussion is unsolved in this research by using KIP. This is the future problem to
3 Some researchers clustered consumers by using blogs about tourism. For example, Pitman et al. (2010) clustered users by a log
analysis and clarified information needs (what kind of information do they need?) of users of website. In that log analysis, they used
retrieval terms of a portal site for tourists and behaviors in that portal site as variables. However, their researches didn’t use natural
languages as data. A target of a review of this paper is only research that analyzes natural languages of blogs by using a text mining. We
don’t have to review all research that deal with blogs as data.

be answered.
3 Methods
I obey the procedure which Kato & Ishikawa (2011) proposed, except for labeling (step 6). I use theprincipal component analysis in step 6 to create labels that express customers' needs by the small
numbers of words. First, I show and explain summary of this Kato & Ishikawa Procedure (KIP) by
using figure 1. KPI is summarized as Figure 1.
Figure 1 Kato & Ishikawa Procedure (KIP)
In the first step, we collect data. We search the blog sites
1. Data Collection
by using the keyword in this step. This keyword expresses
the market we would like to analyze. This keyword is
Keyword Selection
named as target keyword. In this research, this target
2. Product Keywords Selection
keyword is “Huis Ten Bosch”. I gather all blog texts of goo
3. Personal Keywords Selection
blog which authors, who used target keyword in their
blogs not less than one time, wrote. These collected blog
Author Clusteringtexts are analyzed by morphological analysis. As the result,
this analysis makes these blog texts into words and these
4. Nested Analysis
words are used in the following procedure.
5. Author Analysis
In the second step, I choose words that are similar with
6. Labeling
Huis Ten Bosch from words gathered in step 1. The
selected words are first dimension of segmentation. We
call these words product keywords.
In the third step, I select words from words gathered in step 1. These words characterize blog
authors. I call these words personal keywords. This is the second dimension. In the fourth step, I
categorize authors by using product keywords and personal keywords. I call this step nested analysis.
If you do an author analysis in step 5, you do not need to execute step 4.
I can segment markets by nested analysis. But categorizing customers who have high level loyalty
or low level loyalty is to give marketers more useful information. In the fifth step, I divide all authors
into high level loyalty authors (loyal authors) or low level loyalty authors (longtail authors). And I
cluster these authors by each loyalty levels.
First, I categorize authors by using product keywords. Next, I select loyal authors and longtail
authors from authors clustered. Finally, I segment the selected authors by personal keywords. This isauthor analysis.
In the sixth step, I put labels on the loyal authors and longtail authors for easy interpretations. This
step is labeling. In this step, I use principal component analysis for easy labeling. This is KIP.
4 Results
In the first step, we gather data as follows. Target keyword is “Huis Ten Bosch”. I search blog sites
by using goo blog search engine. As the result, I got 907 blog entries. And then, I collect all blog entries
which authors of these blog sites wrote. I got 522 blog authors.
The time period of the collected data is March, 9, 2004 to September, 18, 20114. I extract blog text
4 Some dates of blogs that we gathered are unrealistic. For example, these years are 0000 or 1968. In this research, we decided asfollows. The starting day of a goo blog site is March, 9th, 2004 (http://pr.goo.ne.jp/detail/290/ we checked on October, 3rd, 2011). We
check dates of data that we retrieved and decided that the period of blogs are between that starting day of a blog site and the day that wegathered data. Needless to say, the unrealistic dates of blogs we gathered do not mean that data of blogs are nonsense. So we used theseblogs as data in this research.

from the blog entries, analyze these blog texts by morphological analysis and get words from theseblog texts. If I cannot get blog entries more than 90% of all blog entries, I omit authors who wrote
these entries from 522 authors5. Finally, I get 487 authors, 517126 blog entries, and 914109 words.
In the second step, I choose product keywords. I define that product keywords are similar words
with target keyword “Huis Ten Bosch”. This step is relevant to a blog entry as one unit and is not
relevant to an author as one unit. I employ words which is more similar with Huis Ten Bosch than thethreshold level as product keywords. I use a cosine measure as similarity. I calculate a cumulative
relative score of similarity and accept words that are more than 80% of that score. The threshold levelis 0.00606941840946 and selects 10230 words as product keywords.
In the third step, I choose personal keywords. First, I gather blog entries by each author. And then I
define the words that one author uses but other authors do not use very much, as personal keywords. I
employ the words which characterize an author more than the threshold level. The threshold level is332.456518859 and selects 10230 words as personal keywords6.
The fourth step is an author analysis. An author analysis means categorization by each category.
That categorization includes loyalty of a product. First, I cluster blog entries by each author. Then, I
categorize these authors by product keywords that are selected in step 2. This categorization createsgroups that each authors used similar words in the point of product keywords. I group authors into
four groups by product keywords.
I calculate a relative ratio of significant scores of product keywords by each authors of each thesefour clusters, and then calculate mean scores of the 25 % of upper side by each cluster. I define thecluster which has a max value of mean scores as loyal authors. They have high level loyalty for the
product. The number of loyal authors is 104. In contrast, I define authors who have the minimum
value of loyalty score as longtail authors. The number of longtail authors is 116.
Each author categorized by product keywords is grouped by personal keywords that are selected instep 3. This categorization creates four groups from 104 loyal authors and 116 longtail authors. I
finish segmenting markets by using blog data. However, I just finish clustering authors. I can notinterpret these groups. So I label each four groups of loyal authors and longtail authors.
The fifth step is a labeling. Four groups of loyal and longtail authors include 79465 words (18authors), 107624 words (64 authors), 142748 words (14 authors), and 32168 words (8 authors). Fourgroups of longtail authors include 229550 words (33 authors), 255159 words (61 authors), 80253 words(10 authors), and 343445 words (12 authors). I unify four groups of loyal authors to one group and fourgroups of longtail authors to one group. As the result, loyal authors have 243382 words and longtail
authors have 672155 words.
I calculate values of signification of each word by using p-value of chi squared. The smaller thep-value is, the smaller a probability of independence is. So I can understand that a small p-value of aword shows that word characterizes the loyal authors or longtail authors. At this time, I calculate chisquared value of each word by each cluster for a later analysis.
I choose more important words that characterize authors than other words. The criterion is to selectwords with smaller p-value. The selected words are 0.1 % of all words (calculating to one decimal place
and rounding down if the number is less than 5. As the results, I select 243 words from 243382 wordsof loyal authors and 672 words from 672155 words of longtail authors. I execute a principal component
analysis to 243 words and 672 words by using values of chi squared as data. Loyal and longtail
5 We don't have a rigorous criterion of acceptance of blogs as data. We used over 90% as a criterion in this research, but we used over
99% or 100% as a criterion in the previous research. We don't determine a rigorous criterion because the number of blogs we can get
depends on the condition of networks.
6 We have no criteria to decide threshold level. So we use the same number of words as product keywords.

authors consist of four clusters respectively. I extract four principal components that characterizethese clusters by principal component analysis.
Table 1: Results of Principal Component Analysis
1：Cumulative Proportion of Loyal Authors
Standard
deviation
Proportionof Variance
Cumulative
Proportion

PC1

PC2

PC3

PC4

12.7038

6.87505.86073.288e-15

0.66420.19450.14140.000e+00

0.66420.85871.00001.000e+00

3：Cumulative Proportion of Longtail Authors
Standard
deviation
Proportionof Variance
Cumulative
Proportion

PC1

PC2

PC3

PC4

17.48614.9578

11.9378

1.366e-14

0.455

0.33290.21210.000e+00

0.455

0.78791.00001.000e+00

2：Principal Component Score of Loyal Authors
PC1

PC2

PC3

PC4

[1,]

-7.083819

9.3607982-1.7104123

1.224715e-15[2,]-5.520820-2.9946382

8.0173692

6.234943e-14[3,]-6.426851-6.7142967-5.9774905-5.556319e-14[4,]19.031490

0.3481367-0.3294664-5.833875e-15

4：Principal Component Score of Longtail Authors
PC1

PC2

PC3

PC4

[1,]

-15.325082

18.031077

2.023248

7.261986e-15[2,]-1.951507-13.072880

14.492036

2.358357e-15[3,]-7.630563-11.503896-14.464335

9.755217e-15[4,]24.907152

6.545699-2.050949-1.729671e-14

From the result of principal component analysis in table 1, the first and second principal
components capture 85.9% of cumulative proportion in loyal authors and 78.8% of cumulative
proportion in longtail authors. Almost 80% of loyal and longtail authors are explained by the first andsecond principal components. So I employ the first and second principal components for interpretingthe needs of loyal and longtail authors.
I interpret these two components as follows. I find out the word with the maximum absolute valueof eigenvector in the first and second component. If the value of eigenvector is positive, I sort words
and select 50 words in descending order. On the other hand, if the value of eigenvector is negative, I
sorted I sort words and select 50 words in ascending order. I use these 50 words for interpretingcustomers’ needs.
Labels of loyal authors consist of a medical information ( for example, www, http, jp, PDF, youtube,
cigarette research institute, new type, virus, information, specimen, influenza, blogspot, public welfare,
US, Ministry of Labor, hospital, etc) and foreign resorts (for example, aviation, Tokyo, Chiba, Narita,
Monaco, resort, Monte Carlo, hotel, etc ). On the other hand, labels of longtail authors consist of blogs(for example, diary, archives, seesaa, sale, SeesaaBlog, blog, umbrella, size, doll, html, livedoor, entry,
FC, Blog, etc) and political and economic issues (for example, political party, last year, bank, finance,
Diet, natural disaster, United States of America, the House of Representatives, March, committee,case, administration, prime minister, people, policy, bill, etc). These are the labels for loyal and longtail
authors.
5 Discussions and Conclusion
I clarify the following finding from the results of blog analysis. Loyal authors consist of medical
information and foreign resorts. Therefore, if managers of HTB (Huis Ten Bosch) would like to satisfy
needs of customers of high level loyalty, they should pay their attention to providing of medical
information and management of foreign resorts. In the case of medical information, managers cangive foreign tourists information about a diffusion of influenza and can make them relief. Or the
managers decide to enter the markets of health tourism, to make alliance with hospitals in Sasebo city,and to give advanced medical treatments for foreign tourists under the western atmosphere. As theabove, managers can provide information that is related to a health tourism.
Next, customers who have high level loyalty use words related to foreign resorts, for example
Monaco. From this fact, HTB is under competitive situation with these foreign resorts. Additionally,
we can image that there are many European style resorts in Asia. To win this competition, managers
must differentiate with these resorts. We can get this insight from these results.
On the other hand, longtail authors consist of two axes which are blogs and political and economic
issues. From the fact that longtail authors use words that are relates to blogs, we can image that
customers usually check blog sites. Managers had better to transmit information about daily eventsand attractions by using social media. Because some customers are interested in political andeconomic issues, managers can start a short term education about Japanese business and economic
issues by using facilities of HTB. For example, they can collaborate with national and international
universities for providing the executive MBA by using facilities of HTB. I can clarify these needs.
Additionally, Volo (2010, p. 309) and Pan et al. (2007, p.43) pointed out that we need to develop astandard and automatic procedure to collect and analyze blog texts. I can show you one evidence that
how Kato & Ishikawa’s Procedure (KIP) works well for solving this problem. KIP was already applied
to some kinds of markets and was improved in other papers. I hope that this procedure becomes aworld standard procedure of blog text mining in marketing through more improvements.
Lastly, I summarize limitations for future research. I categorize authors by using product keywords,
personal keywords, and levels of loyalty. However, managers would like to analyze customers’ needs
more in detail for planning marketing strategies. So I try to mix the results of blog text mining with amethod of questionnaire for more detail analysis in future research.
＜Reference＞[1] Carson, Dean., (2008), “The `Blogosphere' as a Market Research Tool for Tourism Destinations: A
Case Study of Australia's Northern Territory”, Journal of Vacation Marketing, April, Vol. 14, No. 2,
pp.111-119.
[2] Kato, Junichi and Masahiro Ishikawa., (2011), “Semi-Automatic Procedure for Market
Segmentation by Using Massive Weblog Data”, The 2011 Spring National Conference ofOperations Research Society of Japan, pp.104-105 (In Japanese).
[3] Kotler, Phillip and Kevin Keller. (2006), Marketing Management, 12th Edition, Pearson PrenticeHall.
[4] Liaoa, Shu-hsien., Yin-Ju Chena and Min-Yi Dengb., (2010), “Mining Customer Knowledge forTourism New Product Development and Customer Relationship Management”, Expert
Systems with Applications, Vol. 37, Issue 6, pp. 4212-4223.
[5] Mitamura, Tamotsu., Wataru Iwasa, and Keiko Yukawa., (2008), “The Study of Tourism Informatics
Using Blog”, Tourism and Informatics, 4 (1), pp.57-65 (In Japanese).
[6] Pan, Bing., Tanya MacLaurin, John C. Crotts., (2007), “Travel Blogs and the Implications for
Destination Marketing”, Journal of Travel Research, August, Vol. 46 No. 1, pp.35-45.
[7] Pitman, Arthur., Markus Zanker, Matthias Fuchs and Maria Lexhagen., (2010), “Web Usage
Mining in Tourism? A Query Term Analysis and Clustering Approach”, Information andCommunication Technologies in Tourism 2010, pp. 393-403.
[8] Volo, Serena., (2010), “Bloggers’ Reported Tourist Experiences: Their Utility as a Tourism Data
Source and Their Effect on Prospective Tourists”, Journal of Vacation Marketing, Vol. 16,
pp.297-311.