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Title

クラスタリングに基づく弱教師あり学習によるレビューの暗黙的属性の分類

Author(s)AYE AYE MAR

CitationIssue Date

2024-03

Type

Thesis or Dissertation

Text version

ETD

URL

http://hdl.handle.net/10119/19067

RightsDescription

Supervisor: 白井 清昭, 先端科学技術研究科, 博士

Japan Advanced Institute of Science and Technology

Doctoral Dissertation

Implicit Aspect Classification of Online Reviews by
Clustering-based Weak Supervision

Aye Aye Mar

Supervisor: Kiyoaki Shirai

Graduate School of Advanced Science and Technology
Japan Advanced Institute of Science and Technology[Information Science]

March, 2024

Supervised by:Professor SHIRAI Kiyoaki

Reviewed by:Professor SHINNOU Hiroyuki
Professor NGUYEN Minh Le
Professor INOUE Naoya
Professor OKADA ShogoAbstract

Aspect Term Extraction (ATE) which is a process of extracting an aspect(also known as opinion target) from a customer review sentence plays a vitalrole in Aspect-Based Sentiment Analysis (ABSA). Many previous work of ATE
focused on explicit aspect but only a few work considered to extract implicit
aspects. However, customer reviews containing implicit aspects are widespread on
the Web (such as Amazon.com) and these sentences are also important to fully
understand the opinions and sentiments of the customers.
One of the bottleneck problem of implicit aspect extraction is lack of a large
dataset of reviews annotated with implicit aspects. Although the corpus annotatedwith implicit aspects is required for every domain due to different types of aspects
in different domains, constructing a corpus is labour intensive and time consuming.
Therefore, a system to automatically construct a dataset annotated with implicit
aspects is required. This study proposed a novel approach that automaticallyconstructs a dataset annotated with implicit aspects using unlabelled Amazon
reviews to address the challenge of implicit aspect extraction. To the best of ourknowledge, no prior work has been performed on the automatic construction ofsuch a dataset.
The goal of this study is to develop a system of ATE for implicit aspects.
A dataset labeled with implicit aspects is automatically constructed by guessing
implicit aspects in unlabeled review sentences. The proposed method involves
clustering review sentences labeled with explicit aspects (which were extracted
by CRF model trained on golden explicit review sentences) and unlabeled review
sentences. In this study, using a K-means clustering approach with a relatively
large number of clusters (10% of total review sentences) aims to generate manysmall but accurate clusters.
Cluster labels, considered as implicit aspects, are automatically assigned basedon the assumption that sentences with similar context share a common aspect.
When selecting the most relevant cluster label among the explicit aspects in thecluster, the frequency of the aspect in the list of aspects extracted by CRF and its
occurrence in the review sentences within the cluster are considered to determineithe relevance of the chosen cluster label. When there is more than one aspect thatcan be the cluster label, we did not consider such kind of cluster since the cluster
label is not unique. Moreover, the reliability of the cluster label to be chosen wasdetermined by the threshold value (Tr ). Unlabeled sentences in clusters matching
pre-defined implicit aspect categories are then obtained as implicit-aspect-labeled
sentences. To increase the number of clusters related with the implicit aspects,
the aspect synonym list was identified.
The accuracy of the constructed corpus was evaluated by a human annotator by checking manually on 50 random sentences for each implicit aspect. The
results showed that accuracy of the sentences in the constructed corpus was reasonably high, i.e., from 0.58 to 0.82. The study presents findings and observations
regarding with constructing the corpus annotated with implicit aspects.
In this study, implicit aspect extraction problem is formulated as classification
problem. Then, BERT model is fine-tuned for implicit aspect classification using
the constructed dataset by investigating the best values of hyper-parameters. Experiments results of implicit aspect classification show that our method achieves
82% and 84% accuracy for the mobile phone and PC reviews respectively, whichare 20 and 21 percentage points higher than the baseline.
Furthermore, the study explores the impact of explicit review sentences for
implicit aspect classification by combining the explicit sentences and implicit sentences and then by training classification model on the combined dataset. The
experimental results showed that it further boosts the performance of implicit
aspect classification in both phone and PC domain.
Keywords: Aspect-based Sentiment Analysis, Aspect Extraction, Implicit
Aspect, Weakly-supervised Learning, Online ReviewiiAcknowledgements

My studies at Japan Advanced Institute of Science and Technology (JAIST)
have been truly incredible. None of this would have been possible without thegenerous support of many individuals.
Firstly, I would like to express my deepest gratitude to my esteemed supervisor,
Professor SHIRAI Kiyoaki, for his kind and invaluable supervision, unwavering
support, continuous patience and encouragement throughout my doctoral degree
program. His expertise and dedication have been instrumental in achieving myresearch goal and have been a source of inspiration to me. Without his mentorship, insights and counseling, I could not have completed my Ph.D. study. I am
particularly grateful for the peaceful and inspiring environment he created, fostering growth both personally and academically. Despite the hardship, my Ph.D.
student life under his excellent mentorship has been rewarding.
I would like to extend my deep appreciation to my second supervisor, Professor
NGUYEN Minh Le who also served as one of the committee members of my
Ph.D. defense from JAIST, for his valuable comments and suggestions. I wish
to send my sincere thanks to my minor research supervisors Professor TOJO
Satoshi and Professor TANAKA Kiyofumi for supporting me kindly.
Words cannot express my heartfelt gratitude to the committee members of my
Ph.D. defense, Professor SHINNOU Hiroyuki from Ibaraki University, Professor INOUE Naoya and Professor OKADA Shogo from JAIST for their fruitfulfeedback, comments, and suggestions.
I am grateful to Senior Lecturer KERTKEIDKACHORN Natthawut fromour Laboratory for his counseling, insightful suggestions, helpful comments, andkind support.
I genuinely would like to express my profound gratitude to the Ministry of Education, Culture, Sports, Science, and Technology (MEXT) for graciously awarding
me a scholarship. Their generous support has played a pivotal role in enabling myacademic pursuits, and I am truly thankful for the opportunities this scholarshiphas provided me.
I sincerely thank professors and lecturers for teaching interesting courses. Furiii

thermore, I would like to extend my thanks to staff members for consistently
creating a safe, convenient, and wonderful environment at JAIST.
I am thankful to my friends for their kind support and for being there to shareboth my joys and challenges.
I would like to express my appreciation to the unknown significant others(USOs) who have contributed to my academic journey, whether through direct orindirect support, in completing my studies.
Lastly, I am profoundly grateful to my family for their unwavering love, support, and understanding.

iv

Contents
Abstracti

Acknowledgementsiii

List of Figuresvii

List of Tablesviii

1 Introduction

1

1.1

Research Background . . . . . . . . . . . . . . . . . . . . . . . . . .

1

1.2

Research Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . .

4

1.3

Research Questions . . . . . . . . . . . . . . . . . . . . . . . . . . .

5

1.4

Chapter Organization . . . . . . . . . . . . . . . . . . . . . . . . . .

5

2 Literature Review

7

2.1

Implicit Aspect Extraction . . . . . . . . . . . . . . . . . . . . . . .

2.2

Dataset of Implicit Aspects . . . . . . . . . . . . . . . . . . . . . . 11

2.3

Weakly Supervised Learning . . . . . . . . . . . . . . . . . . . . . . 13

3 Proposed Method

7

16

3.1

Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 16

3.2

Construction of Dataset Annotated with Implicit Aspects . . . . . . 18

3.3

3.2.1

Explicit Aspect Extraction . . . . . . . . . . . . . . . . . . . 19

3.2.2

Clustering of Sentences . . . . . . . . . . . . . . . . . . . . . 24

3.2.3

Cluster Label Identification . . . . . . . . . . . . . . . . . . 27

3.2.4

Implicit Sentence Retrieval . . . . . . . . . . . . . . . . . . . 28

Implicit Aspects Classification by BERT . . . . . . . . . . . . . . . 30

v

3.4

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31

4 Evaluation
4.1

4.2

4.3

32

Evaluation of Dataset Annotated with Implicit Aspects . . . . . . . 32
4.1.1

Experimental Setup of the Construction of the Dataset . . . 32

4.1.2

Results of Constructed Dataset . . . . . . . . . . . . . . . . 34

4.1.3

Error Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . 37

Evaluation of Implicit Aspect Classification . . . . . . . . . . . . . . 38
4.2.1

Experimental Setup of Implicit Aspect Classification . . . . 38

4.2.2

Results of Implicit Aspect Classification . . . . . . . . . . . 40

Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49

5 Conclusion

50

5.1

Summary of This Study . . . . . . . . . . . . . . . . . . . . . . . . 50

5.2

Answer for Research Question . . . . . . . . . . . . . . . . . . . . . 51

5.3

Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 52

List of Publications

53

Bibliography

54

A Full list of synonyms

66

vi

List of Figures
3.1

Overview of proposed method . . . . . . . . . . . . . . . . . . . . . 18

3.2

Flowchart of constructing corpus annotated with implicit aspects

3.3

Example of cluster . . . . . . . . . . . . . . . . . . . . . . . . . . . 26

4.1

Example of sentences labeled with implicit aspects . . . . . . . . . . 36

4.2

Precision of implicit aspect extraction in phone domain . . . . . . . 42

4.3

Recall of implicit aspect extraction in phone domain . . . . . . . . . 43

4.4

F1-score of implicit aspect extraction in phone domain . . . . . . . 44

4.5

Precision of implicit aspect extraction in PC domain . . . . . . . . 45

4.6

Recall of implicit aspect extraction in PC domain . . . . . . . . . . 46

4.7

F1-score of implicit aspect extraction in PC domain . . . . . . . . . 47

vii

. 19

List of Tables
3.1

Definition of the categories of implicit aspects . . . . . . . . . . . . 16

3.2

Examples of implicit sentences for mobile phone . . . . . . . . . . . 17

3.3

Examples of implicit sentences for PC . . . . . . . . . . . . . . . . . 17

3.4

Example of an extracted sentence labeled with IOB format . . . . . 22

3.5

Example of sentences obtained by explicit aspect extraction . . . . 24

3.6

Examples of synonyms. . . . . . . . . . . . . . . . . . . . . . . . . . 29

4.1

Results of explicit aspect extraction by CRF . . . . . . . . . . . . . 33

4.2

Details of constructed dataset with implicit aspects (phone domain) 35

4.3

Details of constructed dataset with implicit aspects (PC domain) . 35

4.4

Statistics of the training and test data. . . . . . . . . . . . . . . . . 39

4.5

Optimized hyperparameters. . . . . . . . . . . . . . . . . . . . . . . 40

4.6

Results of implicit aspect classification (phone domain). . . . . . . . 41

4.7

Results of implicit aspect classification (PC domain). . . . . . . . . 41

A.1 All synonyms of aspects for mobile phone domain . . . . . . . . . . 67
A.2 All synonyms of aspects for PC domain . . . . . . . . . . . . . . . . 68

viii

Chapter 1
Introduction
1.1

Research BackgroundNowadays, people are more interested in online shopping and their reviews about
the products has become the information that draw attention not only to consumers but also manufactures and researchers. While some customers write theiropinions and feelings online about the products they bought, some customers areseeking out the the opinions of the other customers about the product they areinterested in. Besides the customers, manufacturers are also listening to the customer voices about their products to infer how their products and services areperceived [46]. The exponential growth of digital contents, particularly a substantial volume of online reviews, coupled with the increasing interest of bothcustomers and manufacturers, has significantly amplified the importance of sentiment analysis. It has now become a prominent research area among researchers.
Sentiment analysis, also known as opinion mining, is a subfield of natural
language processing (NLP) to determine and analyze the sentiments, attitudes,
opinions, or emotions expressed in written text. The primary goal of sentiment
analysis is to identify the polarity (positive or negative) of opinions written incustomer reviews [46]. According to Hu and Liu [25], there are three kinds ofreview formats which are are available on the Web.
• Format 1 - Pros and Cons: The format in which the reviewers describe Pros
and Cons separately about the product. The reviews from C|Net.com are
written in this format.
1• Format 2 - Pors, Cons and detailed review: The format in which the reviewers write the Pros and Cons separately as well as description in detials
about the product. The reviews from epinion.com and MSN are written inthis format.
• Format 3 - free format: the format in which reviewers describe freely about aproduct without separating Pros and Cons. The reviews from Amazon.com
are in this format.
Many studies have been done on sentiment analysis on reviews [4, 5, 63, 23,
34, 37, 76, 65, 64, 1, 68, 40, 67, 74, 14, 2, 29, 20]. There are three levels of
sentiment analysis which are document-level, sentence-level and aspect-level (alsoknow as feature-level) [36, 39]. In the document-level, the overall polarity ofa document is decided as a positive opinion or negative opinion based on theopinion words in the document by considering the whole document as one topic[47, 72]. In the sentence-level, the polarity of a whole sentence is decided without
computing the sentiment for each aspect described in the sentence [75]. The
fine-grained level sentiment analysis is aspect-level sentiment analysis which isalso called Aspect-Based Sentiment Analysis (ABSA) [70, 42, 73, 56]. It aims at
inferring the sentiment of a customer at a fine-grained level by determining thepolarity on each aspect of a product, such as “price” and “battery” of a mobile
phone. ABSA plays a vital role not only for customers but also for manufacturers,
because it allows customers to find the strong and weak points of a product inwhich they are interested, while manufacturers can identify the customers’ needs
and expectations accurately.
In general, ABSA consists of two subtasks: Aspect Term Extraction (ATE) and
Aspect Polarity Classification (APC). The former involves extracting aspects of a
product from a sentence in a review, while the latter aims at classifying whether
a customer expresses a positive, neutral, or negative opinion about each extracted
aspect. Two kinds of aspects should be considered in ATE: explicit and implicit
aspects. Explicit aspects are those that appear as explicit words or phrases in thereview sentences, while implicit aspects are expressed implicitly, without directly
mentioning the name of the aspect [23]. Explicit aspect expressions are nouns andnoun phrases. However, implicit aspect expressions are not just adjectives, ad2

verbs, verbs and verb phrases; they can also be very complex [36]. Some examplesof these are as follows.
S1 The battery of the phone lasts many hours, so it does not need to chargefrequently.
S2 I don’t use it any more, as I get tired of always recharging after using justfor a few hours.
Both the sentences S1 and S2 mention the same aspect of the mobile phone, the“battery”. S1 contains the explicit aspect “battery” and directly expresses anopinion about it, whereas S2 implicitly expresses an opinion about the battery
without using the word “battery” itself. In the second sentence, the battery is an
implicit aspect.
Many research has been done explicit aspect extraction from review sentences[79, 25, 24, 43, 7, 28, 45, 50, 9]. Although there has been a great deal of work on
extracting explicit aspects, the identification of implicit aspects has not been vigorously studied [71, 53, 48, 59, 41, 13]. Customer reviews typically offer straightforward information (explicit), but they can also include subtle or undisclosed details(implicit) that are important to take into account since neglecting these implicit
aspects may lead to inaccurate decisions or recommendations for individual users[69]. Implicit aspects are also important in order to fully understand the opinions
and sentiments of customers, since customer reviews containing implicit aspects
are widespread on the internet. Zhang and Zhu showed that 30% of the reviewsin their corpus contained implicit aspects [80]. Similarly, Cai et al. showed that
44% of the review sentences about laptop PCs contain implicit aspects or implicit
opinions [6]. Implicit review sentences are more complex than explicit ones [36].
In addition, capturing the most significant implicit aspects heavily relies on theaccessibility of domain-specific knowledge [52]. On the other hand, different people implicitly describe their sentiments about products using different kinds oflinguistic expressions and writing styles, meaning that implicit aspects are moredifficult to handle in ABSA than explicit ones.
The lack of a large dataset of reviews annotated with implicit aspects is one of
the bottlenecks for implicit aspect extraction. Most current methods of ATE rely3

on supervised learning, in which an aspect extraction model is trained on a labeleddataset [38]. Although explicit aspect extraction were performed with Support Vecotr Machine (SVM) [31] and deep learning model such as Convolutional Neural
Network (CNN) [30] and Bidirectional Encoder Representations from Transformers (BERT) [9] by using standard SemEval-2016 dataset that was annotated with
explicit aspects, the extraction of implicit aspects cannot be performed in thesame way when there is no dataset labeled with implicit aspects.

1.2

Research Objectives

The goal of this study is to develop a system of ATE for implicit aspects. A
dataset labeled with implicit aspects is automatically constructed by guessing
implicit aspects in unlabeled review sentences. Then, a model for implicit aspect
extraction is obtained using the pre-trained language model. We demonstrate theeffectiveness of our proposed method when it is applied to customer reviews in two
domains: mobile phones and PCs, and present our findings on the complex natureof the implicit aspects and problems in the construction of the dataset through anerror analysis. Our contributions can be summarized as follows.
• We propose a novel weakly supervised method to construct a dataset automatically labeled with implicit aspects. To the best of our knowledge,
no prior work has been performed on the automatic construction of such adataset.
• We train a model for implicit aspect extraction by fine-tuning a pre-trained
language model using the above dataset coupled with existing review sentences including explicit aspects.
• We empirically evaluate the constructed dataset as well as the performanceof implicit aspect extraction achieved by our proposed model.
The most notable contribution is the first one. This study is the first attempt toautomatically construct a corpus annotated with implicit aspects.

4

1.3

Research Questions

Our major research question is as follows.
How to develop a model for implicit aspect extraction without using alabeled dataset?
To answer this research question, we consider a weakly supervised approach. We
construct a dataset labeled with implicit aspects automatically, then we train an
implicit aspect extraction model from this dataset. In addition, we investigate
the additional use of an existing dataset labeled with explicit aspects for implicit
aspect extraction.
The above major research questions can be decomposed into three sub-researchquestions as follows.
RQ1 How to automatically construct a corpus annotated with implicit aspects,
which is sufficiently large for training a model?
RQ2 How is the performance of implicit aspect classification by a model trained
from an automatically constructed corpus?
RQ3 Is it possible to improve the performance of implicit aspect extraction using
both datasets of implicit and explicit aspects?

1.4

Chapter Organization

This dissertation is structured as follows. Chapter 2 gives the literature reviewsby divining into two categories. Firstly, we describe related work about implicit
aspect extraction task. Then, we introduce previous work regarding with datasetof implicit aspects. We present the most closely related work with our researchand clarify the difference between their work and our work.
Chapter 3 explains our proposed system by firstly introducing the overview
architecture and explanation. Next, step by step processes of automatically constructing the corpus annotated with implicit aspects are presented with the illustration of flowchart diagram. Then, we fine-tune the BERT model with our5constructed corpus and realize implicit aspect classification by the trained BERT
model.
In Chapter 4, we evaluate our proposed system. Firstly, we described the experimental setup of the construction of the dataset. Then, the accuracy of theconstructed corpus is checked and computed by a human evaluator and results arepresented. Next, we make error analysis of constructed corpus and presented our
findings and observations. We also conduct an experiment to evalute the performance of implicit aspect classification. First, we describe the experimental setup
of implicit aspect classification and optimize hyper-parameters. Next, the performance of implicit aspect classification model which is trained on three different
types of corpora is investigated.
Finally, Chapter 5 summarizes the dissertation with our main contributions
as well as the remaining problems. We also describe the future work which can
improve some parts of our system.

6

Chapter 2
Literature Review
2.1

Implicit Aspect Extraction

As discussed in the previous chapter, current methods of ABSA mainly focus on
explicit aspects. However, there have been a few attempts to handle implicit
aspects [71, 53, 48, 59, 41, 13].
Fei et al. proposed dictionary-based approach for identifying aspects impliedby adjectives by formulating the problem as collective classification [12]. They
compared their work with WordNet that can attributes given an adjectives andtheir work performed better than WorkNet.
Zainuddin et al. used the dependency relation rules which can be extractedusing dependency parser in order to extract the implicit aspect from hate crime
reviews in Twitter [77]. They applied hybrid approach between association rulemining, dependency parsing and SentiSordNet to perform the aspect-based sentiment analysis.
Qui applied semantic ontology as a method to extract implicit aspects [35].
Within the ontology framework, Qui established semantic relations linking various
aspects to each entity. The identification and extraction of opinion words lacking
explicit features were integral to their approach. The researcher then formulated
equations to measure the semantic similarity between features and opinion words,
leveraging the underlying ontology. Finally, the extraction of implicit features was
conducted based on the associations calculated through this semantic analysis.
Hajar et al. introduced a hybrid methodology that incorporated a corpus,
7

WordNet, and a Naı̈ve Bayes classifier [18]. Initially, the approach involved theextraction of all adjectives from a designated corpus. Subsequently, WordNet was
employed to identify words with lexical connections to these adjectives, encompassing synonyms and antonyms. Finally, the acquired dataset was utilized totrain a Naı̈ve Bayes classifier, facilitating the implicit extraction of aspects.
Many previous studies considered correlations between sentiment words (suchas “excellent” and “bad”) and aspect words. Su et al. [60] applied Point-wise
Mutual Information (PMI) based sentiment association analysis to identify the
implicit aspects. They only presented the PMI scores for some word examples
and few mapping results of some example words. The quantitative experimental
results such precision, recall have not been described in their work.
Hai et al. proposed a co-occurrence association rule mining approach for identifying implicit aspects [17]. The association rule was in the form (sentiment word
→ explicit aspect), indicating that the sentiment word and explicit aspect frequently co-occurred in a sentence. The rules were generated from a review corpus
and converted to more general rules that mapped each sentiment word to a clusterof aspects. The obtained rules were then applied to identify the implicit aspect ofsentences that included not an explicit aspect but a sentiment word. The resultsof an experiment using Chinese review data showed that the F1- score for implicit
aspect identification was 74%.
Zang and Li highlighted a limitation of the above association rule-based methodin that only a single aspect can be associated with a sentiment word by a rule, but
two or more aspects can be related to one sentiment word [78]. For example, since
the sentiment word “good” is a general one, it can express opinions towards many
aspects, such as “battery”, “screen” or “quality”. The contextual informationof the sentiment word is necessary to identify the exact aspect. Based on thisfinding, they proposed a classification-based method for the identification of an
implicit aspect, where the task was formulated as a classification problem. First,
pairs containing an explicit aspect and a sentiment word were obtained by a rulebased method, where the rules were used to extract the pairs from the resultsof a dependency parsing of the review sentences. Then, sentences including an
explicit aspect and a sentiment word were excerpted as a document collection,

8and were labeled with the aspect. Using this document collection as training
dataset, a topic-feature-centroid classifier was trained using bag-of-words features.
They evaluated their method on Chinese reviews of cell phones and cameras on
Amazon, and found that the F1-scores were 74.66% and 78.76% respectively, i.e.,
better than the association rule-based method of Hai et al.[17]. The reason forthis was that their classification-based approach was able to capture the context ofthe sentiment words and infrequent dependencies between aspects and sentimentwords that were not considered in the association rules.
Sun et al. proposed a method which takes into account not only opinion words
but also the context information for extracting implicit features from Chinese
mobile phone reviews and computer reviews [61]. In their method, there are threesteps to extract implicit features. First, they built a co-occurrence matrix toshow the relationship between opinion words and product features. Second, they
determined whether there is an implicit feature, and if so, they found a candidate
set of implicit features. Finally, they found the correct implicit features according
to the candidate implicit features scores calculated based on the opinion words andthe context information of the implicit features. They used the data set containing
3656 reviews from mobile phone users and 2446 reviews from computer users.
They computed the recall and precision for identifying the implicit features bymanual evaluation. For the extraction of features and opining words, their method
outperformed Double Propagation method [51] which propagated the information
through both sentiment words and features.
Bagheri et al. proposed a graph-based method for implicit aspect extraction [3].
The vertices in the graph were either explicit aspects or sentiment words, while theedges between them were weighted based on the number of their co-occurrences
and the degree of the vertices in the graph. To construct the graph, explicit
aspects were extracted using an iterative bootstrapping algorithm, starting with
the initial seed aspects. For a given review sentence, an aspect connected tosentiment words in the sentence with highly weighted edges was extracted as animplicit aspect. Their method was evaluated using a dataset of reviews of fiveproducts, constructed by Hu and Liu [23], and showed that the F1-scores for the
implicit aspect extraction method were between 57% and 71%.

9

Several studies have trained models for implicit aspect extraction using adataset labeled with implicit aspects. Hendriyana et al. proposed the sentencelevel topic model (SLTM) [22]. Their work extracted explicit and implicit featuresfrom dummy reviews and amazon customer reviews from the amazon e-commerce
website, specifically focusing on the Xperia Z smartphone. They described thatamazon customer reviews data they used contain 1407 sentences containing eightfeatures (aspects); camera, battery, endurance, screens, operating system, networks, audio and price. The feature extraction method utilized in their work is
the SLTM (Sentence Level Topic Model). Preprocessing steps, such as lowercasing, tokenizing, stop-word removal, lemmatization, and part-of-speech tagging,were applied to structure the unstructured text data from amazon reviews. Whenapplied to the dummy dataset, the system exhibited a performance of 76% in extracting explicit features and 92.59% in extracting implicit features. Nevertheless,when tested on the amazon customer review dataset, the system’s performanceyielded 88.24% for explicit features and 60% for implicit features. Notably, thesystem can only detect one product feature in a single sentence.
Schouten and Frasincar aimed to find implicit aspects from sentences that
could contain zero or more implicit aspects [57]. A training dataset was first
constructed, and the co-occurrence matrix C of implicit aspects and words wascreated from the training data. For a given sentence, scores for the implicit aspects
were calculated using the matrix C, and the implicit aspect with the highest score
was chosen. However, if the maximum score was less than a given threshold, thesystem judged that the sentence contained no implicit aspect. They demonstrated
the effectiveness of their method using consumer reviews of products and restaurants. They also reported that a large proportion of the sentences in the productreviews had no implicit aspect, meaning that in such cases, a classification-based
approach might not be feasible.
Soni and Rambola proposed a hybrid method incorporating a Recurrent Neural
Network (RNN) that is trained on a dataset prepared by themselves and similarity calculations based on WordNet and similarity function from spaCy to detect
implicit aspects. Their dataset consisted of 700 records obtained from 160 Samsung M21 phone reviews in Amazon. Precision, recall, and F-measure(weighted

10

average) achieved by their proposed method were 48.7, 42.4 and 41.6, respectively.
They described that their method can be improved through dataset enhancement,
adoption of alternative text vector representations, utilization of additional lexical resources, exploration of alternative models, or optimization of parameters.
They supposed that the dataset should contain more records since 700 records areinappropriate for training and testing the model in their work. Moreover, theymentioned that the dataset should be balanced.
Unlike the previous studies, our proposed method relies on supervised learning
using a pre-trained language model that worked well for various natural language
processing (NLP) tasks. In addition, instead of using a manually labeled dataset,
we use a labeled dataset constructed by a weakly supervised method that requires
no human effort. This enables us to develop a large dataset annotated with implicit
aspects automatically.

2.2

Dataset of Implicit Aspects

Only explicit aspects are annotated in the most commonly used datasets for ABSA,
such as Sentihood [55] and SemEval-2014 Task 4 Aspect Based Sentiment Analysis(we call it “SemEval-2014 dataset” hereafter) [49]. However, a small or pilot
dataset with implicit aspects has been constructed. Hu and Liu developed adataset for ABSA that consisted of corpora based on five product reviews: twodigital cameras, a cellular phone, an MP3 player, and a DVD player [23]. Boththe explicit and implicit aspects were manually annotated. However, Hu andLiu’s dataset was relatively small, and the number of sentences containing implicit
aspects for each of the five products was between 14 and 55. Cruz et al. extended
this dataset by adding annotations of implicit aspect indicators (IAIs), which weresentiment words indicating a certain implicit aspect [10]. They selected sentences
labeled with at least one implicit aspect from Hu and Liu’s dataset, and thenmanually annotated the sentences with the IAIs. They then used the extended
dataset to train a conditional random field (CRF) to extract IAIs from the review
sentences.
Most methods for ABSA are based on supervised learning, which requires a11labeled dataset [66]. In addition, the aspects mentioned in each review are very
different for different product types or domains. To perform ABSA for various
types of products, it is necessary to individually construct a labeled dataset foreach domain. This is our primary motivation for the automatic construction of alarge review dataset annotated with implicit aspects.
There have been a few attempts to automatically construct a dataset with
explicit aspects. Giannakopoulos et al. constructed a new dataset from Amazon
computer reviews [15]. They also proposed a method which used attention mechanism to select sentences from amazon reviews for constructing a dataset with
explicit aspects. In their work, they used attention mechanism to remove somenoisy sentences and predict review ratings. They kept only the reviews that haveat least 3 sentences and at most 10 sentences to leverage attention mechanism.
They assigned sentiment scores to each sentence in the reviews based on review
ratings and the sentences with high sentiment scores were selected to construct a
dataset. For labeling aspect terms in sentences, they used nouns and noun phrases
which appeared less than 30 times in the dataset. Then, they selected the aspects
from noun and nouns phrases candidates by using Senticnet sentiment lexicon andsyntactic rules. The resulting automatically labeled dataset was used to train amodel for aspect term extraction with distant supervision. They trained multiple
classifiers by using their dataset. They used the SemEval-2014 train data as thevalidation data and used SemEval-2014 test data as the test data. They presented
that B-LSTM followed by CRF classifier trained on their constructed dataset
achieved 50.33, 40.49 and 44.87 for precision, recall and F1-score, respectively andoutperformed the supervised baseline method of the SemEval-2014 ABSA contest.
Hadano et al. acquired new training data from non-tagged data for aspects
identification of sentiment sentences by using clustering approach which was basedon cosine similarity measure between the vector of the sentence and vector of
the cluster centroid [16]. They assumed that sentences sharing similarities also
share common aspects. Consequently, they employed a clustering technique togroup these akin sentences together. For clustering implementation, they employed Bayon, a straightforward and expeditious hard-clustering tool. Bayon’s
clustering methodology relies on the iterative process of repeated bisection. Specif-

12

ically, the data is divided into two distinct clusters as the first step. As the second
step, the cluster displaying the lowest similarity between its centroid and each individual element within it is identified. As the third step, two elements from thatcluster are selected randomly. As the fourth step, based on the similarity between
them, all elements in the cluster identified in second step are subdivided into two
separate clusters. As the fifth step, if swapping elements between these clusters
enhances their similarity, swapping is done. Finally, iteration is repeated from thesecond step to the fifth step. In their work, the aspect of sentences in clusters are
determined by an annotator as the representative sentences. At first, the number of annotated sentences was the number of cluster. Based on the assumptionthat the aspects of sentences belonging to each cluster are equal to the aspects of
the representative sentences of each cluster, their system acquired sentences withhigh similarity as new training by applying different similarity range to increases
the number of annotated sentences. They used Support Vector Machine (SVM)
as the classifier and they evaluated their system by using game review and thedata set for evaluation was constructed by one annotator by using annotation
tool. They presented that their proposed method with similarity range ”0.7-0.9”
which produced 219 sentences achieved the best performance with the accuracyof 73.97%.
In [16], Hadano et al. acquired new training data for aspect extraction basedon clustering of sentences, as they assumed that similar sentences shared common
aspects. Our method shares the same idea as this method, that is, both methods
construct a dataset by clustering of review sentences. However, while Hadano’s
method is semi-supervised, where a human annotator determines the aspects ofthe sentences, our method is unsupervised, in the sense that it requires no humanintervention.

2.3

Weakly Supervised LearningThis section firstly describes the types of weak supervision, then explain the difference of our method with other weakly-supervised learning methods.
Typically, there are three types of weak supervision; incomplete supervision,13

inexact supervision and inaccurate supervision [81].
• Incomplete supervision is a kind of supervision where a small amount of labeled data, which is insufficient to train a good learner, are given, while there
are abundant unlabelled data. There are two main approaches for incomplete
supervision; active learning [58] and semi-supervised learning [8, 82]. Active
learning assumes that the ground-truth labels of unlabeled instances can bequeried from an oracle such as a human expert. In semi-supervised learning,
two fundamental assumptions guide the approach to utilizing labeled andunlabeled data: the cluster assumption and the manifold assumption. The
cluster assumption posits that data naturally form clusters, where instances
within the same cluster share the same class label. On the other hand, themanifold assumption suggests that data points reside on a manifold, implying that nearby instances should yield similar predictions. Both assumptions
revolve around the idea that similar data points should produce similar outputs, and the inclusion of unlabeled data aids in uncovering relationships
between similar data points.
• Inexact supervision in machine learning refers to situations where the labeled data provided for training a model is not as precise or accurate as
desired. This can result from noisy labels, partial labels, ambiguous labels,
or uncertain annotations.
• Inaccurate supervision pertains to scenarios where the provided label information for training a model may not consistently represent the ground truth;in essence, some labels in the training dataset may be erroneous. Several
studies focused on how to correct errors in a noisy dataset.
Our weakly-supervised learning method falls under the category of Incomplete
Supervision. Specifically, labeled and unlabeled data are utilized based on thecluster assumption. To the best of our knowledge, our method is the first attemptof weakly supervised method for implicit aspect classification task.
One of the common approaches to tackle the data sparseness problem is semisupervised learning. In the survey by Zhou [81], semi-supervised learning is categorized as Incomplete supervision, i.e., a kind of weakly-supervised learning.
14

In contrast to the conventional semi-supervised approach of combining a small
amount of labeled data with a larger unlabeled set, our research adopts anotherweakly supervised strategy. This involves relying exclusively on a substantial unlabeled dataset without any labeled examples. Unlike traditional methods thatstrategically blend labeled and unlabeled data, our emphasis is on addressing thechallenges posed by the lack of labeled data by fully leveraging the extensive unlabeled dataset. Through our weakly supervised approach, we aim to potentiallyreduce the resources required for obtaining and annotating labeled samples, presenting a distinctive perspective on mitigating dataset scarcity for implicit aspect
extraction.

15

Chapter 3
Proposed Method
3.1

Overview

In this study, the task of implicit aspect extraction is defined as a classification
problem. For a given review sentence, our system chooses a category of the implicit
aspect of which the sentence implicitly expresses the reviewer’s opinion. It is
supposed that those categories of the implicit aspects are pre-defined. For example,
the sentence “I get tired of often recharging it.” is classified into the implicit aspect
category “battery.”In our work, we focus on the identification of implicit aspects for two types of
products: mobile phones (or “phones” in short) and personal computers (PCs).
For each, six or five categories of implicit aspects are defined, as in Table 3.1. The
category “interface” for PCs includes any devices for the human-machine interface,such as a keyboard, track pad, mouse, and so on. Table 3.2 and Table 3.3 showsthe example sample sentences for each implicit aspect for mobile phone and PC,
respectively.
Table 3.1: Definition of the categories of implicit aspects
Product type

Category of implicit aspect

mobile phone

Battery, Case, Look, Price, Screen, Size

PC

Interface, OS, Price, Screen, Software

16

Table 3.2: Examples of implicit sentences for mobile phone
Implicit Sentence

Aspectit get full charge but lose the power in minutes .

battery

it ’s durable and has a strong grip on the phone .

caseaesthetically , he loved it so much we have purchased several .

look

it is so inexpensive and very good value for money .

priceit did n’t feel super secure but has n’t fallen off my daughter ’s

screenphone yet .
this adds a little bulkiness to the phone for people who carry it insize

their pocket like i do .

Table 3.3: Examples of implicit sentences for PC
Implicit Sentence

Aspect: ) the trackpad feels good under the finger , and there ’s a scrollinterfacelever situated between the two buttons . interface
i had not used xp before , and find the improvement over windows98

OS

to be analogous to the improvements of windows98 over windows95.
would cost more to send it back than i paid , so i suppose we ’ll

price

all just have to deal with it until i am in the market for something
more reliable .
my lcd display went bad after a month of use , which makes me

screenwonder how long this notebook will last past its warranty period .
because of the way the recovery media is designed i could not install
any of the sony provided applications onto the newly loaded system.

17

softwareFigure 3.1: Overview of proposed method
Figure 3.1 shows an overview of our proposed method. Since no large-scale
dataset labeled with implicit aspects is publically available, it is automaticallyconstructed. Specifically, from a large number of unlabeled reviews and a public
dataset of reviews labeled with explicit aspects, the “weakly supervised dataset
constructor” module automatically extracts review sentences with implicit aspects
to form a dataset labeled with the implicit aspects in a weakly supervised way.
This module is essential in this study; the details are described in section 3.2. Next,
a classifier of implicit aspect is trained from the obtained dataset. The details ofthese procedures are described in section 3.3. In the inference, an implicit aspectof a test sentence is identified by the trained classifier.

3.2

Construction of Dataset Annotated with Implicit Aspects

Figure 3.2 shows how the dataset annotated with implicit aspects is constructed.
Amazon reviews are used as unlabeled reviews, while the SemEval-2014 dataset
is used as a set of reviews with explicit aspects. The dataset is constructed infour steps. First, explicit aspects are extracted from the Amazon reviews using an
aspect extraction model trained from the SemEval-2014 dataset [49]. Second, aclustering of the sentences of the reviews is performed, where sentences that mention the same aspect, regardless of whether it is implicit or explicit, are intended
to be merged into a cluster. Third, a label is determined for each cluster: it is the
aspect of the reviews in that cluster. Finally, the sentences labeled with implicit
aspects are retrieved to form the dataset. The details of these steps are presented18

in the following subsections.

Figure 3.2: Flowchart of constructing corpus annotated with implicit aspects

3.2.1

Explicit Aspect Extraction

The goal of explicit aspect extraction is to extract, from unlabeled Amazon reviews, words and phrases that explicitly represent an aspect of a product. To
achieve this, a model to extract explicit aspects is obtained by supervised learning. The dataset of SemEval-2014 Task 4 ABSA is used as the training data.
Although there are four subtasks in Task 4 ABSA, subtask 1, “aspect term extraction,” is the most appropriate for this study. All aspect terms in the review
sentences are marked up in the dataset. The task organizers provide two domainspecific datasets: one for laptops, and the other for restaurants. Each consistsof around 3,000 reviews. This is one of the largest publicly available datasets
for ABSA. In the present study, the laptop dataset is used for extracting explicit
aspects for the PC domain. It is also used for the mobile phone domain, since
there is no available dataset of ABSA for the phone domain. Note that the disagreement of the domains, between the training and test data, may decrease theperformance of the explicit aspect extraction. A possible solution is to apply adomain adaptation technique that enables us to train an accurate classification
model from training data of a different domain.

19

Conditional Random Field
Explicit aspects are extracted by applying Conditional Random Field (CRF) [33,32, 26, 62]. It is known that CRF performs relatively well even when it is trainedon a small training data and it showed comparatively good results for the task ofaspect extraction from reviews in SemEval shared task related to ABSA, where twobest results were based on CRF [49]. Moreover, CRF has two main advantages.
The first one is independence of assumptions of the observed varibles. The second
one is that CRF avoids the label bias problem. CRF has a single exponential
model for the joint probability of the entire sequence of labels given the observation
sequence. Therefore, even if some data is missing, the observation sequence canstill be labeled with less number of features [54].
CRF is a type of discriminative undirected probalistic graphical model. It is
used to encode known relationship between observations and construct consistentinterpretations. Let X = (x1 , ....xn ) be the sequence of observed data. Let Y =(y1 , ...., yn ) be the sequence of random variables associated with the vertices of thegraph. CRF models a conditional probablility P (Y |X) over hidden sequence Y
given observation sequence X. That is the conditional model is trained to label
an unknown observation sequence X by selecting the hidden sequence Y which
maximizes p(Y |X). Then the conditional distribution p(Y |X) can be formalized
as follows:!
X
1
P (Y |X) =exp
λc fc (yc , X) ,
Z(X)
cϵC

(3.1)

where C is all set of all graphs cliques, fc is set of all features, λc is its corresponding
weight. Z(x) is a normalization function as follows:!
Z(X) =

X
y

exp

X

λc fc (yc , X) .

(3.2)

cϵC

Labelling and Features
CRF accepts a review sentence (sequence of words) as input and identifies a label
for each word as output. Job and Gurevych [27] represented the possible labels
20

following the Inside-Outside-Begin (IOB) labelling schema, where B, I, and O
stand for beginning of an aspect word or phrase, continuation (inside) of an aspect
word or phrase and other words which are not aspect tokens, respectively. As weapplied sequential labelling, each word in the sentences in SemEval-2014 laptop
dataset is assigned with one of the IOB labels. Table 3.4 shows IOB format of an
example sentence “The phone would keep making the charging notification soundwhich caused the screen to also turn on thus killing the battery .” The explicit
aspects in this sentence are “charging notification sound”, “screen” and “battery”.

21

Table 3.4: Example of an extracted sentence labeled with IOB format
Word

POS tag

IOB tag

The

DT

O

phone

NN

O

would

MD

O

keep

VB

O

making

VBG

O

the

DT

O

charging

VBG

B

notification

NN

I

sound

NN

I

which

WDT

O

caused

VBD

O

the

DT

O

screen

NN

B

to

TO

O

also

RB

O

trun

VB

O

on

IN

O

thus

RB

O

killing

VBG

O

the

DT

O

battery

NN

B

.

.

O

22

We used the sklearn-crfsuite library with the default settings to train CRF
model. The features used for training the CRF model are in the follwoing list.
Those features are extracted from the previous, current, and succeeding words.
POS tagging of each word in the review sentences was done by Natural Language
Toolkit (NLTK)1 .
• word in lower case• part-of-speech (POS) of a word• last two characters of POS of a word
• flag whether all characters are upper case• flag whether the initial character is upper case• flag whether it is a digit• last two characters (of the current word only)• last three characters (of the current word only)
Extracting Amazon Review Sentences with Aspect Information
By applying the trained CRF model, review sentences annotated with explicit aspects are extracted from Amazon unlabeled review sentences. In addition, review
sentences from which no explicit aspect is extracted are also retrieved. These sentences might include no aspect, but sometimes could include an implicit aspect.
In other words, the sentences without the explicit aspects can be the potential
sentences including the implicit aspects. As a result, a set of review sentenceswith and without explicit aspects is obtained at the explicit aspect extraction
step. Table 3.5 shows examples of extracted sentences.2 The second and third
sentence contain the explicit aspect of “screen” and “price” respectively, whileother sentences include no explicit aspect.
1
2

https://www.nltk.org/
In this thesis, outputs of the tokenizer are shown as example sentences. For example, “does’t”

is split into two tokens “does” and “n’t”.

23Table 3.5: Example of sentences obtained by explicit aspect extraction
Review sentence

AspectIt does n’t click with the white piece at all, and it easily slides offnone

I wanted so much to keep this case on, but I also did n’t wan na risk

screenmy phone having a giant crack on the screen due to a case that doesn’t stay on
Or the price, it is neat, but I really doubt I ’m going to keep it on

price

my phone
What a bummer

none

It is cute and light weight

none

3.2.2

Clustering of Sentences

The review sentences, either labeled with explicit aspects or unlabeled, were thenmerged into clusters. The goal of this clustering is to make clusters of sentencesthat express opinions about the same aspect.
Each review sentence is converted to Sparse Composite Document Vectors(SCDVs) [44]. Sparse Composite Document Vectors (SCDVs) emerge as a sophisticated and highly effective method for encapsulating the rich semantics embedded
in review sentences, surpassing the simplicity of traditional word embedding averages. Notable for their excellence in document representation, SCDVs achievea sparse yet information-rich encoding through a meticulous fusion of global andlocal TF-IDF weighting. This unique characteristic not only enhances computational efficiency but also captures the intricate nuances of language composition.
Significantly, SCDVs excel in capturing semantic similarity, particularly in discerning implicit sentiments within review sentences. This capability positions
SCDVs as a potent tool for extracting the subtle meanings embedded in textual
data, contributing to a deeper understanding of reviews. In the broader context
of document analysis, SCDVs offer a versatile and robust approach, promising toadvance the precision and depth of document representation across various applications within the field of natural language processing, thereby enriching thelandscape of computational linguistics and information retrieval.
24

We chose k-means as the clustering algorithm, since it is an efficient method[19]. The algorithm follows an iterative process that converges towards an optimal
solution, aiming to minimize the intra-cluster variance and maximize inter-cluster
separation. The fundamental idea behind k-means is to assign each data point
to the cluster whose centroid is the nearest, based on a chosen distance metric,
typically Euclidean distance.
The algorithm initiates by randomly selecting k initial centroids, where k represents the predetermined number of clusters. Subsequently, data points are assigned
to the cluster whose centroid is closest, and the centroids are recalculated basedon the mean of the assigned data points. This assignment and centroid updateprocess iterates until convergence, signifying stability in cluster assignments.
In our application of k-means to sentence clustering, the vectors representing
sentences in a high-dimensional space are analogous to data points. The Euclidean
distance, as the chosen metric, quantifies the dissimilarity between sentence vectors. This distance-based approach is conducive to grouping similar sentences into
clusters, making k-means particularly suitable for tasks such as sentiment analysis
and review clustering.
One critical consideration in implementing k-means is the determination of
the optimal number of clusters, denoted by k. In this study, it is not preferable
to merge the sentences referring to different aspects into one cluster. In otherwords, the purity of the clusters should be high. Therefore, we set the numberof clusters to a relatively large number so that we could create many small but
accurate clusters of review sentences. Specifically, the parameter k is set to 10 %of the total number of review sentences.
In summary, the application of k-means in our proposed methodology is rooted
in the foundational principles of unsupervised learning, iterative optimization, anddistance-based clustering. This theoretical foundation serves as the basis for thesubsequent application of the algorithm to review sentence clustering, aligningwith our goal of extracting meaningful insights from diverse aspects of the review
data.

25

1) For the prices, was n’t worth sending back & is really for those few times
away from home or do n’t have outlet handy & the battery gets really low
anyway . (price, battery)
2) Like it but it causes the battery to get really hot and lock the phone .
(battery)3) I really like the design, but however the casing did not snap nicely withmy phone in place . (design)4) took a while to get to me its really cute just hard to come off which isgood and bad i guess good because its secure if you drop the phone andbad because you may have to use something to get it open to clean orswitch cases in any event i like its hard rubber and design . (hard rubber,design)5) I would have given it one star since it really does n’t hold a charge oreven charge for that matter , but I decided to add another for the designof the case although the kickstand is extremely flimsy and half of the time
wo n’t even hold up my phone . (none)

Figure 3.3: Example of cluster
Figure 3.3 shows an example of a constructed cluster. It consists of five review
sentences. In general, a cluster contains two kinds of sentences. One is a sentence
containing one or more explicit aspects, such as the sentences 1)–4) in Figure 3.3,
where the explicit aspects extracted by the CRF model are indicated by being inparentheses. The other is a sentence that does not contain explicit aspects, suchas the sentence 5). The explicit aspects that do not correspond to any pre-defined
aspect categories (and their synonyms that will be explained in subsection 3.2.4),
such as “hard rubber”, are ignored.

26

3.2.3Cluster Label Identification

The task of cluster label identification involves choosing the most significant aspect for a sentence cluster. This is not always obvious, since there are two or
more explicit aspects in the cluster as shown in Figure 3.3. Algorithm 1 shows
pseudocode for this process.
Algorithm 1: Algorithm for cluster label identification
Input : Cluster of review sentences
Output: Cluster label
1

Let si be a sentence in the cluster, and ai be the explicit aspect of si ;2

Let F re(ai ) be the frequency of ai in the cluster;3

Let Oc(ai ) be the number of occurrence of ai in the set of sentences {si };4label ← aspect with the maximum F re(ai );5

if label is unique then6
7

return ReliabilityCheck(label )else

8Let {a′i } be the set of aspects with maximum F re(ai );9label ← aspect with the maximum Oc(a′i );10

if label is unique thenreturn ReliabilityCheck(label )

11
12

else
return INDETERMINABLE

13
14

end

15

end

16

def ReliabilityCheck(label )17

if Rel(label) ≥ Tr thenreturn label

18
19

else
return INDETERMINABLE

20
21
22

end
end

27

The basic idea of Algorithm 1 is that the most frequent aspect is chosen as thecluster label. Two kinds of frequency, F re(ai ) and Oc(ai ), are considered. F re(ai )is the number of the times an aspect ai is extracted, while Oc(ai ) is the number ofoccurrences of the aspect in the review sentences. The explicit aspects in a cluster
are compared with respect to F re and Oc in this order, then the most frequent
one is chosen as the cluster label (lines 4–11). If two or more aspects have thesame F re and Oc, the label is defined as INDETERMINABLE (line 13), whichindicates that the cluster may be wrongly made up of sentences about different
aspects.
Next, we measure the reliability of the label, which is defined as the proportionof the sentences with the explicit aspect to all sentences in the cluster, as shownin Equation (3.3).
Rel(aspect) =F re(aspect)number of sentences in the cluster(3.3)

If the chosen aspect is reliable enough (the reliability is greater than or equal toa threshold Tr ), it is chosen as the cluster label; otherwise, the label is defined as
INDETERMINABLE (lines 16–22). The threshold Tr was empirically determinedfor each aspect category in the experiment.
Let us explain how the label of the example cluster in Figure 3.3 is identified. F re(design)=2 since the explicit aspect “design” is extracted twice, andOc(design)=3 since the word “design” appears three times in the sentences. Similarly, F re(battery)=2 and Oc(battery)=2. Since F re(design) and F re(battery)are the same, Oc is compared. Then “design” is chosen because Oc(design)> Oc(battery). Finally, the reliability is assigned the measure Rel(design)=2/5.
If it is higher than Tr , “design” is chosen as the label of the cluster.

3.2.4

Implicit Sentence Retrieval

The last step is to collect the sentences containing implicit aspects. As explained
in section 3.1, six implicit aspects for the phone domain and five implicit aspects
for the PC domain have been defined. For each implicit aspect, the cluster whoselabel coincides with it is chosen. To get more clusters, a list of synonyms of theimplicit aspects is created manually, and clusters for which the label is a synonym
28

are also chosen. For example, synonyms of the aspect “battery” are “battery case,”“battery life,” “power,” and so on. Table 3.6 shows examples of the synonyms.
The full list of the synonyms are shown in the second column of Tables A.1 andA.2 in Appendix A. No synonym is used for “price” and “size” of the phone
domain and “price” of the PC domain. Note that the cost of constructing the listof the synonyms is much less than the manual annotation of many sentences with
implicit aspect labels.
Table 3.6: Examples of synonyms.
(a) Phone domain
AspectSynonymBattery

battery case, battery life, power

Casecase quality, case cover

Look

design, color

Price

—

Screenscreen protector, screen cover

Size—(b) PC domain

AspectSynonymInterfacekeyboard, touchpad

OSwindows, windows xp

Price

—

Screenmonitor, screen size

Softwareprogram, applications

Sentences for which no explicit aspect has been extracted are then retrieved
from the chosen clusters. A cluster label is attached to these retrieved sentences as
their implicit aspects. In the example in Figure 3.3, sentence (5) is retrieved withthe label “look” as its implicit aspect, since the cluster label is “design”, which isa synonym of “look”.
Recall that the number of the clusters in k-means is set to a large value (10%29

of total sentences). Our motivation for this is to avoid making clusters containing
multiple aspects, since they cause errors in the process of the cluster label identification and retrieval of implicit sentences. Although sentences with the same
aspect may be scattered to different clusters, it might not be a problem because
we can retrieve sentences with the implicit aspect from each cluster.

3.3

Implicit Aspects Classification by BERT

A classifier for implicit aspect identification is trained using the constructed dataset.
Bidirectional Encoder Representations from Transformers (BERT) [11] is chosen
as our classification model, since it has achieved outstanding performance formany NLP tasks. The bert-base-uncased3 is chosen as the pre-trained BERT
model. Then it is fine-tuned using the dataset of implicit aspect sentences weconstructed.
In addition, the SemEval-2014 dataset is also used for fine-tuning. Although
it contains not implicit but explicit aspects, the linguistic expressions in sentenceswith explicit aspects may be similar to those of the implicit aspects. Thus the
explicit aspect sentences can also be used for fine-tuning BERT, resulting in an
increase in the number of the training samples. To form the training data for the
implicit aspect classifier, the review sentences with an explicit aspect that agrees
with one of the pre-defined implicit aspect categories are excerpted. Similar to theretrieval of implicit aspect sentences described in subsection 3.2.4, soft-matching
using a list of synonyms of the implicit aspect is applied to retrieve more sentences.
The synonyms for each implicit aspect category are manually and exhaustively
excerpted from the SemEval-2014 dataset. The complete list of the synonyms isshown in the third column in Tables A.1 and A.2 in Appendix A. In addition, areview sentence is not included in the training data when it contains two or more
implicit aspect categories. For example, let us consider the sentence “this laptop
is a great price and has a sleek look.” Since it contains two aspects, “price” and“look,” it is not included in the training data for the phone domain. Note that
it is included in the training data for the PC domain, since only “price” is an3

https://huggingface.co/bert-base-uncased30

implicit aspect category for this domain.
In summary, the BERT models of implicit aspect classification are trained
from two different datasets: one is the dataset of the implicit aspects created byour proposed method, the other is the union of this dataset and another corpus
extracted from the SemEval-2014 dataset.

3.4

Summary

In this chapter, we presented the overview of our proposed method, including theoverview architecture of our method. Then, the flowchart describing the main
steps in our approach was described and each step was explained in detail withsome examples. We presented our proposed approach by dividing into two mainparts; construction of the dataset annotated with implicit aspects and classification of the implicit aspects for unknown reviews. The first part consisted offour steps; explicit aspect extraction, clustering of review sentences, cluster label
identification and implicit sentence retrieval. The second part was the proposal of
training implicit aspect classification using BERT model, where both the proposed
dataset labeled with implicit aspects and the existing dataset labeled with explicit
aspects.

31

Chapter 4
Evaluation
This chapter reports the results of experiments to evaluate our proposed method.
First, in section 4.1, the quality and quantity of the dataset annotated with implicit
aspects described in section 3.2 is assessed. Next, in section 4.2, the performanceof the classification of the implicit aspect by the method described in section 3.3
is evaluated.

4.1

Evaluation of Dataset Annotated with Implicit Aspects

4.1.1

Experimental Setup of the Construction of the Dataset

Amazon product data [21] was used to construct the dataset. We excerpted
30,000 review sentences from the category entitled “Cell Phones” and “Accessories” for the phone domain, and 10,000 review sentences from the category
entitled “computers” for the PC domain.
The CRF model for explicit aspect extraction was trained first. The laptop
reviews from the SemEval-2014 dataset were used for training the CRF model
for both the phone domain and the PC domain. A preliminary evaluation of
the performance of the CRF model was carried out on the SemEval-2014 dataset,
where 90% of the datasets was used for training and 10% for testing. The precision,
recall, and F1-score for explicit aspect extraction by CRF were shown in Table 4.1.
They were sufficiently high for the subsequent procedures.
32

Table 4.1: Results of explicit aspect extraction by CRF

Precision

Recall

F1-score

0.77

0.64

0.70

For each aspect category, we randomly chose 50 sentences associated with thetarget implicit aspect (or all of them when the number of such sentences was lessthan 50). The chosen sentences were then manually judged so as to determine
whether they expressed users’ opinions about the implicit aspect. In our experiment, a human evaluator judged the sentence without referring its surrounding
context. As an evaluation criterion, we used the accuracy, defined as the ratioof the correct review sentences containing implicit aspects to the total number ofmanually checked sentences.
Tr is the parameter used in Algorithm 1, where the cluster label is judged
as INDETERMINABLE when the ratio of the majority aspect in the cluster islower than Tr . If we set Tr higher, the number of the retrieved implicit sentences
will be reduced, but the accuracy will be improved. In this experiment, Tr was
initially set to 0.1. For some implicit aspect categories, we set Tr higher when
the accuracy was relatively low. We argue that it is not necessary to optimize Tr
using validation data. Once Tr is set so that the accuracy is high, we can easily
increase the number of sentences with implicit aspects by using more unlabeled review sentences. However, empirical investigation of how the increase in unlabeled
sentences can contribute to enlarge the dataset and to improve the performanceof the implicit aspect classification should be carried out in the future.

33

4.1.2

Results of Constructed Dataset

Tables 4.2 and 4.3 show the statistics of the constructed dataset as well as the accuracy
and Tr for the phone and PC domains, respectively. The third column shows the average
size of the clusters and the standard deviation in the form of ave±sd. As for the phone
domain, we obtained 290 clusters for six aspects, and the average size of the clusters (thenumber of sentences per cluster) was between 8 and 12. Recall that each cluster consistsof sentences with both explicit and implicit aspects; the numbers of each are shown in
the fourth and fifth columns, respectively. As a result, from 90 to 393 implicit sentenceswere obtained for six aspect categories. As for the PC domain, 149 clusters were obtained
in total. The number of the obtained implicit sentences was between 45 and 261 for fiveaspect categories.
Next, we discuss the accuracy of the obtained sentences including implicit aspects.
The accuracy was 0.58 or more for all aspect categories in the phone domain. The threshold Tr was set higher for the “screen” and “price” categories to improve the accuracy. As
for the PC domain, the accuracy of the five aspect categories was between 0.56 and 0.72.
Tr was set to 0.3 for the “price” category, but 0.1 (the default value) for the others.
Figure 4.1 shows examples of sentences with implicit aspects. The labels for the clusters are (price) and (look), and the check marks indicate the obtained implicit sentences,in which the cluster label (price or look) is annotated as the implicit aspect. Sentence(4) in cluster 1 was successfully annotated with the aspect “price”, although the word“price” was not explicitly used. The sentences with check marks in cluster 2 are othergood examples of the implicit aspect of “look”. Note that the cluster label was identified as “look” since the majority of the explicit aspects in this cluster were identified as“design”, which is a synonym for the aspect category of “look”. In summary, the results
indicate that our proposed method is promising in terms of automatically constructing adataset annotated with implicit aspects.

34

Table 4.2: Details of constructed dataset with implicit aspects (phone domain)
Aspect# of

Average

# of

# of

clusters

size of

Explicit

Implicit

cluster

Sentence

Sentence

Accuracy

Tr

Battery

58

12±16

274

393

0.82

0.1

Case

23

8.6±5.5

104

94

0.74

0.1

Look

62

11±9.2

303

353

0.58

0.1

Price

89

11±10

751

252

0.78

0.4

Screen31

8.7±11

179

90

0.76

0.2

Size

27

8.4±6.2

121

106

0.70

0.1

“#” means the number of clusters or sentences.

Table 4.3: Details of constructed dataset with implicit aspects (PC domain)
Aspect# of

Average

# of

# of

clusters

size of

Explicit

Implicit

cluster

Sentence

Sentence

Accuracy

Tr

Interface24

9.0±5.4

117

100

0.62

0.1

OS

25

12±9.0

145

163

0.72

0.1

Price

15

8.6±5.8

84

45

0.56

0.3

Screen4411±7.5

228

261

0.70

0.1

Software41

10±7.0

147

250

0.64

0.1

“#” means the number of clusters or sentences.

35

Cluster 1: (price)
1) Great price, great service from the vendor . (price, service)2) Cheap price for a good quality made item . (price, quality)
3) Very pleased with this item and it was an exellent price ! (price)4) This was such a nice small and cheap item , I had to order 2 of them , just to have
one in each car . (none) ✓5) for its price, it’s not too bad, with a beautiful design (price, design)6) good item for great price . (price)
Cluster 2: (look)1) The color options are awesome and its very portable . (color options)2) Very vivid colors and the car charger is an awesome bonus . (car charger)
3) The design is amazing and the lettering is a little light but that does n’t matter aslong as it fit and you are satisfied with your purchase , because I was ! (design)4) The design was ok for a cheap case , but it was not the color it should have been !
! ! ! (design)5) This case is beautiful and vibrant in color , it has somewhat of a grip so it does n’t
slip out of your hands easily . (none) ✓6) I ’ve always had plain solid colors , but when I saw this I thought it would look nice
. (none) ✓7) ONLY THING NICE ABOUT THIS ITEM IS THE ARRAY OF COLORS . (none)✓
8) A great buy as it does not slip out of your hand and has an awesome vivid design .
(none) ✓9) Nice design and color . (Nice design)10) like the design and color . (design)11) I love the leopard design and colors defiantly makes my phone unique ! (leopard
design)12) I love the design and colors . (design)13) The colors are vibrant , the design is unique , and the case snaps together easily
and is actually hard to pry back off ( I tried ! (design)14) I do like this Owl & case and the colors and the design is great also . (design)
Figure 4.1: Example of sentences labeled with implicit aspects
36

4.1.3

Error Analysis

We found some major causes of error in the process of implicit aspect identification forthe phone domain. When we initially set the threshold Tr to 0.1, numerous errors were
found in the extraction of the implicit aspect “price.” This was because “price” is arather general concept, and frequently occurred with other aspects, such as “service,”“battery,” “case,” or “look.” For example, in Cluster 1 in Figure 4.1, sentences 1), 2)
and 5) include “price” with other aspects. In this example of a cluster, sentence 4) was
correctly extracted as a sentence with this implicit aspect, but many sentences in otherclusters were wrongly extracted. However, by setting Tr to 0.4, the accuracy was improved
to 0.78, although this was offset by a decrease in the number of extracted sentences.
“Screen” was another implicit aspect for which we found many errors. Even whensentences contained the explicit aspect “screen,” they often mentioned not the screen
itself but other related concepts, such as notifications or information shown on the phone
screen. However, by changing Tr to 0.2, the accuracy was improved to 0.76. In addition,
errors were caused by ambiguity in the meanings of words. For example, the word “look”
was used both to represent the design of the mobile phone and as a verb that was almost
equivalent to “seem” (as in the phrase “looks like ...”). Another problem was ambiguityin the aspect itself; for example, the word “cover” was ambiguous, and could have meant“phone cover” or “screen cover.”We also found some major causes of errors in the process of implicit aspect identification for the PC domain. Five percent of the manually assessed sentences labeled with
the implicit aspect “software” were written about RAM. RAM might be related to thesoftware since it enables some software and applications to run quickly. However, RAM
itself is not software but hardware. Besides, 79% erroneous sentences for the category “interface” mentioned a port such as “USB port” and “serial port”.1 They originated fromone cluster that consisted of many sentences including the word “port.” The label of thiscluster was identified as “interface” since some sentences included both “keyboard” (asynonym of “interface”) and “port,” e.g., “Because your keyboard itself has 2 USB ports,
you can plug your mouse and printer into your keyboard.” Accidental co-occurrence of1

Note that the implicit aspect “interface” is defined as a man–machine interface such as a keyboard,mouse, and trackpad in this study. The interfaces to connect other devices (e.g. USB port, display port)
are not included.

37

an aspect and another word (such as “keyboard” and “port”) could be a cause of theincorrect assignment of the implicit aspect.

4.2

Evaluation of Implicit Aspect Classification

4.2.1

Experimental Setup of Implicit Aspect Classification

The performance of the proposed model for classifying of implicit aspects was evaluated.
First, the test data was constructed by the following procedures. As described in subsection 4.1.1, a few sentences in the constructed dataset labeled with implicit aspects weremanually evaluated. For each aspect category, 30 (or 10 when the number of extracted
implicit sentences is small) sentences were randomly chosen from the sentences that were
judged as correct. Thus the test data consisted of genuine sentences with implicit aspects
where the distribution of the aspect categories was relatively balanced.
Next, three datasets were constructed as follows.
De

A set of review sentences with explicit aspects. It was made from the SemEval-2014
dataset as described in section 3.3.

Di

A set of review sentences with implicit aspects. It was constructed by our proposed
method as described in section 3.2.

De+i A set of both sentences with explicit and implicit aspects.
Table 4.4 (a) and (b) show the number of sentences in De , Di , and De+i as well as thetest data for the phone and PC domain, respectively.
Three classifiers were obtained by fine-tuning BERT using these datasets. Hereafter,
the models trained from De , Di , and De+i are denoted by Me , Mi , and Me+i , respectively.
These three models were compared in this experiment, where Me is the baseline model.

38

Table 4.4: Statistics of the training and test data.
(a) Phone domain
AspectDatasetTest Data

De

Di

De+i

Battery

106

363

469

30

Case

4

64

68

30

Look

21

323

344

30

Screen

93

80

173

10

Size

21

96

117

10

Price

82

222

304

30

Total

327

1148

1475

140

(b) PC domain
AspectDatasetTest Data

De

Di

De+i

Interface

83

70

153

30

OS

45

133

178

30

Price

83

35

118

10

Screen

88

231

319

30

Software

104

220

324

30

Total

403

689

1092

130

When we fine-tuned BERT, the hyperparameters were optimized on the validation
data. Specifically, the dataset in Table 4.4 was randomly split into 90% for the training
data and 10% for the validation data. The optimized hyperparameters and their possible
values are as follows:• batch size: {8, 16, 32}• learning rate: {2e−5 , 3e−5 , 4e−5 , 5e−5 }• number of epochs: {2, 3, 4, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50}

39

The best set of the hyperparameters was chosen by several criteria on the validation
data. More concretely, the criteria were checked in the following order: (1) the highest
accuracy, (2) the highest macro average of F1-score for all aspect categories, and (3)the lowest validation loss. The best hyperparameters for each dataset are presented inTable 4.5. The final BERT model was fined-tuned using the overall dataset (both thetraining and validation data) with the optimized hyperparameters.
Table 4.5: Optimized hyperparameters.
DomainPhone

PC

Dataset

De

Di

De+i

De

Di

De+i

Batch size

8

8

8

8

8

8

Learning rate

3e−5

5e−5

4e−5

4e−5

5e−5

5e−5

Number of epochs

20

30

35

5

35

50

4.2.2

Results of Implicit Aspect Classification

Tables 4.6 and 4.7 show the results of the classification of implicit aspects. These tables
present the precision (P), recall (R), and F1-score (F) for each aspect category, their macro
average, and the accuracy (micro average). The best score among the three models isshown in bold. Graphical representations of precision, recall and F-score of Table 4.6
are shown in Figure 4.2, Figure 4.3 and Figure 4.4, respectively. Similarly, Figure 4.5,
Figure 4.6 and Figure 4.7 illustrates the precision, recall and F-score of Table 4.7.

40

Table 4.6: Results of implicit aspect classification (phone domain).
Aspect

Me

Mi

Me+i

P

R

F

P

R

F

P

R

F

Battery

0.68

0.90

0.77

0.96

0.87

0.91

0.91

0.97

0.94

Case

0.38

0.30

0.33

0.79

0.50

0.61

0.82

0.47

0.60

Look

0.68

0.57

0.62

0.65

0.80

0.72

0.74

0.87

0.80

Price

0.92

0.80

0.86

0.94

0.97

0.95

0.94

0.97

0.95

Screen

0.39

0.70

0.50

0.89

0.80

0.84

0.62

0.80

0.70

Size

0.43

0.30

0.35

0.53

0.90

0.67

0.75

0.90

0.82

Macro avg.

0.58

0.59

0.57

0.79

0.81

0.78

0.80

0.83

0.80

Accuracy

0.62

0.79

0.82

Table 4.7: Results of implicit aspect classification (PC domain).
Aspect

Me

Mi

Me+i

P

R

F

P

R

F

P

R

F

Interface

0.95

0.60

0.73

1.00

0.67

0.80

0.83

0.83

0.83

OS

0.82

0.30

0.44

0.89

0.83

0.86

0.93

0.83

0.88

Price

0.67

0.40

0.50

0.86

0.60

0.71

0.86

0.60

0.71

Screen

0.73

0.80

0.76

0.89

0.80

0.84

0.90

0.90

0.90

Software

0.39

0.80

0.53

0.52

0.83

0.64

0.72

0.87

0.79

Macro avg.

0.71

0.58

0.59

0.83

0.75

0.77

0.85

0.81

0.82

Accuracy

0.61

0.77

41

0.84

Figure 4.2: Precision of implicit aspect extraction in phone domain

42Figure 4.3: Recall of implicit aspect extraction in phone domain

43

Figure 4.4: F1-score of implicit aspect extraction in phone domain

44

Figure 4.5: Precision of implicit aspect extraction in PC domain

45

Figure 4.6: Recall of implicit aspect extraction in PC domain

46

Figure 4.7: F1-score of implicit aspect extraction in PC domain

47

Our model Mi outperformed the baseline model Me for all evaluation criteria
except for the recall of “battery” for the phone domain, the precision of “look”
for the phone domain, and the recall of “screen” for the PC domain. In addition,
large differences between Mi and Me were found. The macro average of the F1score and the accuracy of Mi for the phone domain was better than those of Me
by 0.21 and 0.17 points, respectively. Similarly, the improvement of 0.18 pointsof the macro F1 and 0.16 points of the accuracy was found for the PC domain.
Therefore, the corpus of sentences with implicit aspects, which was constructedby our proposed method, was an effective training dataset for implicit aspect
classification. It seems reasonable, since both the test data and Di consisted of
implicit sentences, while De consisted of explicit sentences.
Comparing models Mi and Me+i , it was confirmed that the use of both thesentences with explicit and implicit aspects could further boost the performanceof the classification. As for the phone domain, Me+i outperformed Mi by 0.02
points with respect to the macro average of the F1-score and 0.03 points withrespect to the accuracy. However, when the McNemar test was performed tocheck the difference between Mi and Me+i , it was not statistically significant in 95%
confidence level. Besides, further improvement was found in the results for the PC
domain; Me+i was better than Mi by 0.05 points in the macro F1 and 0.07 points inthe accuracy. It was confirmed by the McNemar test that the difference between
them was statistically significant in 95% confidence level. Since the reviews in
SemEval-2014 dataset are in the laptop domain, adding the explicit sentences
to the implicit sentences can remarkably contribute to improve the classification
performance in the PC domain, but not in the phone domain. In addition, the F1score of Me+i was worse than that of Mi in two aspects: “case” and “screen” forthe phone domain. Adding the sentences with explicit aspects to the dataset madeby the sentences with implicit aspects did not always contribute to improving theperformance.
Our best model Me+i was always better than the baseline (Me ) with respect to
the F1-score of all aspect categories. As for the macro average of F1-score and theaccuracy, improvements by 0.23 and 0.20 points were found. These results provethe effectiveness of our proposed method.

48

We discuss the reason why Me , the model trained from the sentences with
explicit aspects, performed poorly. One obvious reason is the lack of training
samples. For example, there were only four sentences for the aspect “case” in De
for the phone domain, thus the F1-score of the model Me was low, 0.33. Anotherreason may be the disagreement of the domains. Recall that the dataset De was
extracted from the SemEval 2014 ABSA laptop dataset. When it was applied tothe phone domain, the domains of the training and test data were different. We
observed that the aspect “screen” in the phone and PC domains referred to different concepts, although it was the same aspect for electronic devices. Customers
pay attention to a protector, cover, fingerprint or swipe on the screen in the phone
domain, while they focus on resolution or size of a screen in the PC domain. Thismight be the reason why the F1-score for “screen” in the phone domain was low,viz., 0.50. On the other hand, in our approach, the sentences with an implicit
aspect were extracted from unlabeled data of the same domain. Therefore, theproblem of the domain-shift can be alleviated. In addition, the increase in thenumber of the samples in the training data can obviously contribute to improve
the performance of the classifier.

4.3

Summary

In this chapter, we have presented the experimental setup and the results of two
main tasks of our approach; constructing the corpus annotated with implicit aspects and classification of implicit aspects. We did qualitative and quantitative
analysis for our constructed corpus and error analysis was also presented. We
presented how we chose the best hyperparameters for fine-tuning BERT classifier
for the implicit aspect classification task. We evaluated the performance of implicit classification by our proposed method and baseline method and discussed
the reasons of results of our method and baseline. We experimented our proposed
method in two domains: mobile phone domain and PC domains. The results haveshown that our method was better than the baseline method significantly.

49

Chapter 5
Conclusion
5.1

Summary of This Study

This paper has proposed a weakly-supervised leaning method to classify a sentence in a customer review into pre-defined categories of implicit aspects. First,
a dataset annotated with implicit aspects was automatically constructed. For agiven unlabeled dataset consisting of sentences with explicit and implicit aspects,
clustering was performed to merge sentences having the same (explicit or implicit)
aspect. Then, the explicit aspect was transferred to a sentence within the samecluster as a label of an implicit aspect. Next, the constructed dataset and theexisting dataset with explicit aspects were used to fine-tune the BERT model toidentify an implicit aspect of a sentence. The results of an experiment on two
domains (mobile phones and PCs) showed that our proposed model, trained fromthe weakly labeled dataset, outperformed the baseline, trained from the sentenceswith explicit aspects only, by a large margin. An error analysis was also carriedout to reveal the major problems in the construction of the implicit-aspect-labeleddataset.
We would like to discuss the difference between our proposed method andother weakly-supervised methods. As discussed in section 2.3, our method wascategorized as Incomplete Supervision using a small amount of labeled data and alarge amount of unlabeled data, where the gold labels of the unlabeled data were
guessed by the cluster assumption. In conventional weakly-supervised approach,a small amount of data annotated with implicit aspects might be used. In our50

weakly supervised method, any labeled data annotated with implicit aspects was
not used. Instead, the sentences with the explicit aspects were used as the initiallabeled data, then the explicit aspects were transferred to the labels of implicit
aspect sentences. No use of labeled data was our important challenge.

5.2

Answer for Research Question

As described in section 1.3, our major research question is “How to develop a model
for implicit aspect extraction without using a labeled dataset? ”. It was decomposedto three sub-research questions RQ1, RQ2, and RQ3. We answer these researchquestions as follows.
RQ1 How to automatically construct a corpus annotated with implicit aspects,
which is sufficiently large for training a model?
An automated approach to construct a corpus annotated with implicit aspects was developed by the following procedures. We clustered the labelled
review sentences (which were extracted with explicit aspects by CRF modeltrained with the dataset annotated with explicit aspects) and unlabeledreview sentences. Then, a cluster label for each cluster (which was also
regarded as an implicit aspect) was automatically identified based on the assumption that the sentences with similar context shared a common aspect.
The unlabled sentences in a cluster whose label was coincident with the predefined implicit aspect category were obtained as the implicit-aspect-labeled
sentences. The manually designed synonyms were also used to increase thenumber of labeled sentences. In our clustering approach by K-means, thenumber of clusters (parameter k) is set to a relatively large number (10 %of the total number of review sentences) to obtain many small but accurate
clusters of review sentences. The accuracy of the the constructed corpus wasevaluated by manually checking 50 review sentences for each implicit aspect
category. The accuracy was reasonably high, i.e., from 0.58 to 0.82.
RQ2 How is the performance of implicit aspect classification by a model trained
from an automatically constructed corpus?
51

We presented the method to fine-tune the BERT model for implicit aspect
extraction (called “implicit aspect classification” in this thesis because it was
defined as the classification task). The constructed corpus annotated with
implicit aspects was used as the labeled dataset for fine-tuning of BERT.
The results of the experiments showed that the accuracy of the BERT modeltrained with implicit sentences was 0.17 or 0.16 points better than that with
explicit sentences. It indicated that our corpus was obviously better thanthe existing dataset labeled with explicit aspect.
RQ3 Is it possible to improve the performance of implicit aspect extraction using
both datasets of implicit and explicit aspects?
To answer this research question, we compared the implicit aspect classification performance of Mi (which is the BERT model trained with thedataset conatining implicit aspects only) and Me+i (which is the BERT
model trained with both implicit aspects and explicit aspects). The results of the experiments showed that the integration of implicit sentencesand explicit sentences could further boost the performance of implicit aspect classification. Specifically, Me+i is better than Mi in accuracy by 0.03
and 0.07 points for the phone and PC domains, respectively.

5.3

Future Work

Future avenues of research continuing those of this study include the following:• It is necessary to extend our method of constructing the dataset as well as
implicit aspect classification to handle sentences including multiple implicit
aspects. One of the possible solutions is as follows. Instead of a hard clustering, a soft clustering is applied to allow a sentence to belong to multiple
clusters. It enables us to add multiple implicit aspects for one sentence inthe dataset. Then, we train the model for multi-class classification that canidentify multiple implicit aspects in one sentence.
• The explicit aspects were automatically extracted, but some of them may
be incorrect. On the other hand, the sentences including the explicit aspects52

can be obtained from the existing dataset for ABSA. These sentences can bemixed with unlabeled sentences for the clustering. Such an approach may
improve the performance of the clustering.
• Manual construction of the synonym lists shown in Table 3.6 (also Table A.1
and A.2) can be replaced with an automatic synonym expansion method.
• More appropriate clustering algorithms other than k-means should be investigated.
• The performance of the the implicit aspect classification model, which istrained on the corpus containing the review sentences with explicit aspects
exactly from the same domain, can be further investigated.

53

Publications
Journal• Aye Aye Mar, Kiyoaki Shirai, and Natthawut Kertkeidkachorn. 2023. Weakly
Supervised Learning Approach for Implicit Aspect Extraction. Information
14, no. 11: 612. https://doi.org/10.3390/info14110612
International Conference• Aye Aye Mar and Kiyoaki Shirai. 2022. Automatic Construction of an
Annotated Corpus with Implicit Aspects. In Proceedings of the Thirteenth
Language Resources and Evaluation Conference, pages 6985–6991, Marseille,
France. European Language Resources Association.

54

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Appendix A
Full list of synonyms
Table A.1 and A.2 show all synonyms of the implicit aspects for mobile phone and
PC domains, respectively. These tables include two lists of the synonyms used
for different purposes. The column “Synonyms for soft-matching with cluster
label” shows the synonyms to choose the clusters of review sentences including
the target implicit aspects (as described in Subsubsection 3.2.4), while the column“Synonyms for soft-matching with explicit aspect” shows the synonyms to extract
sentences including the target explicit aspects from SemEval 2014 Task 4 ABSA
dataset (as described in Subsection 3.3).

66

Aspect
Battery

Case
Look
Price

ScreenSize

Table A.1: All synonyms of aspects for mobile phone domain
Synonyms for soft-matching Synonyms for soft-matchingwith cluster labelwith explicit aspectbattery case, battery life, battery battery life, charger, cord, usb port,
percentages, battery access, bat- usb ports, power,power lighttery pack, battery charge, battery
charger, charger, blackberry charger
brand, blackberry charger, USB
charger, USB adapter, cord, USB
cord, USB port, USB ports, USB
plugs, car charger, USB cable, USB
cables, Samsung car charger, quality
charger, power, power port, power
loss, power lightcase quality, case covercase design
design, color
design ,designed, color—
price tag, price range, cost, costing, priced, costed, shipping, budget,
value
screen protector, screen protectors, screen resolutions, screen resoluscreen cover, screen look, precut tion, 18.4” screen, screen graphscreen protectors
ics, looking, service center, seventeen inch screen, 17” inch screen,
17-inch screen, 17 ince screen, 17
inch screen, resolution of the screen,
screen brightness, display, monitor,
surface, stock screen, screen size,acer screen, lcd, lcd screen, screen
hinges,picture quality, resolution on
the screen—
size, sized67

Aspect

Interface

OS

Price
ScreenSoftware1

Table A.2: All synonyms of aspects for PC domain
Synonyms
Synonyms for soft-matching with explicit aspectfor
SM1with clusterlabel
keyboard,
keyboard, touchpad, touch pad, keyboard flex,Keyboard
touchpad,, KEYBOARD, touch pad, keys, mouse, trackpad,left
touchpad, mouse key, key bindings,10-key,regular layout keyboard,
keyboard flex right click key, touch-pad , mouse keys, island backlit
keyboard,multi-touch mouse,multi-touch track pad,Apple
keyboard,mouse on the pad, left button,shift key, mousepointer, flatline keyboard
windows xp windows xp home edition , windows media player, windows
homeedi- xp , windows xp pro , windows convert , operating systemtion,windows, os, Windows 7, operating system , operating systems,
media
XP , Vista , Windows applications , Windows Vista , key
player,windows pad , Mac OS , antivirus software, Windows XP SP2, Winxp,windows
dows 7 Ultimate , OSX 16 , Windows 7 Starter, Windows 7
xp pro, win- Home Premium, Windows 7 Professional, Windows operatdows convert, ing system, Windows operating systems, Windows update,
operating
Windows XP drivers, Window update, Windows, Windows
system, os
Vista Home Premium, Win 7—
price tag, price range, cost, costing, priced, costed, shipping, budget, value
monitor,monitor, screen size, screen real estate, screen flickers,
screenscreen distortion, screen resolutions, screen resolution,size,screen
screen dispaly, 18.4” screen, screen graphics, looking, serreal
estate, vice center, seventeen inch screen, 17” inch screen, 17screen flick- inch screen, 17 ince screen, Screen size, resolution of theers,screen screen, screen brightness,30” HD Monitor, display, Resodistortion
lution, display, surface, stock screen, Acer screen,17 inch
screen, LCD, screen hinges, picture quality, resolution on
the screen
programs,
programs, program,isoftware, applications, software kit,program,software problem, itools software, MS Applications, suite
isoftware,of software, system, Microsoft office for the mac, preloaded
applications,software, Microsoft office, software packages, trackpad,
software kit, Software, antivirus software, Microsoft Word for Mac, MS
software prob- Office, MS Office apps, Dreamweaver, Final Cut Pro 7,
lem,itools Photoshop, Safari, Firefox , MSN Messenger, Apple apsoftware
plications, music software, Office Mac applications, Word,
Excel, software options, Sony Sonic Stage software, Garmin
GPS software, Microsoft Office 2003, powerpoint, iMovie,
iWork, Internet Explorer

soft-matching.

68