EXTRACTION AND REPRESENTATION
MAQBOOL RAMDHAN IBRAHIM AL-MAIMANI
UNIVERSITI TEKNOLOGI MALAYSIA
ENHANCED ASPECT LEVEL OPINION MINING KNOWLEDGE EXTRACTION AND REPRESENTATION
MAQBOOL RAMDHAN IBRAHIM AL-MAIMANI
A thesis submitted in fulfilment of the requirements for the award of the degree of
Doctor of Philosophy (Computer Science)
Faculty of Computing Universiti Teknologi Malaysia
AUGUST 2015
ACKNOWLEDGEMENT
In the name of Allah, Most Gracious and Most Merciful
All praise and thanks are for Allah, and peace and blessings be upon to His messenger, Muhammad (S.A.W).
First of all I am grateful to his Almighty Allah who gave me strength to complete this thesis. All praise goes first only to him as without his help I would have not been
able to reach to this successful end.
Then, I would like to express my sincere and special appreciation to my supervisor Prof Dr Naomie bt Salim who helped me a lot to complete this research. Her valuable advices in this regard are unforgettable and triggered me to archive this vital milestone of my thesis. I am speechless and cannot find the right to words to
express my thanks for her valuable inputs and guidance during my study.
I would like also to express my thanks to my colleagues at UTM and Oman Air who helped me with their moral support to ensure I complete my research and submit it
on time.
ABSTRACT
ABSTRAK
Terdapat keperluan untuk mencari teknik-teknik yang lebih berkesan untuk
mengekstrak, mengelaskan, mewakilkan dan merumuskan pendapat pelanggan dalam
talian terhadap produk dan servis untuk analisis sentimen yang lebih baik. Tesis ini
bertujuan untuk meningkatkan aspek pengekstrakan dan perwakilan pendapat. Kajian ini
menggunakan sumber leksikal SentiWordNet yang khusus dibina bagi perlombongan
pendapat dan digunakan secara meluas dalam analisis sentimen. Kajian ini
memperkenalkan pendekatan yang menggunakan Kata Sifat, Kata Kerja, Kata Adverba
dan Kata Nama (AVAN), bagi menganalisis kesemua jenis sentimen perkataan
berkaitan pendapat dan ia juga tidak hanya terhad kepada Kata Sifat dan Kata Adverba
seperti dalam pendekatan konvensional. Perwakilan pendapat dipertingkatkan dengan
menawan elemen-elemen utama pendapat ke dalam predikat yang terdiri daripada
perkataan pendapat, kekuatan, skor dan kategori bagi meningkatkan perwakilan pendapat
dan klasifikasinya. Seterusnya, perakaunan pendapat telah diperkenalkan untuk
meringkaskan skor pendapat pada tahap kumpulan yang pelbagai. Di samping itu, tesis
ini memperkenalkan satu konsep baru yang dikenali sebagai kekuatan pendapat dengan
mengklasifikasikannya kepada darjah pendapat tertentu. Skor diperuntukkan kepada
pendapat berdasarkan kepada kekuatan pendapat itu dinyatakan. Selain itu, sebagaimana
pendapat adalah kabur dalam alam semulajadi, kajian ini menunjukkan bahawa logik
kabur adalah teknik yang efektif untuk digunakan kerana logik manusia terdapat
kekaburan. Ini adalah penting kerana pendapat tidak seharusnya hanya boleh
dikategorikan dalam sentimen Boolean klasik. Kajian ini mengenalpasti SentiWordNet,
AVAN, Kekuatan Pendapat dan Logik Kabur sebagai ciri-ciri untuk mengelaskan ulasan
pelanggan ke dalam model ramalan 5 kelas (Cemerlang, Baik, Sederhana, Kurang Baik
dan Tidak Baik). Keputusan menunjukkan Pengelas Turutan Pengoptimuman Minimum
menggunakan ciri-ciri ini memberikan 92% lebih ketepatan berbanding teknik sebelum
ini iaitu Mesin Sokongan Vektor dan Regresi Logistik. Selain itu, pengabungan AVAN,
Pendapat Kekuatan dan Logik Kabur mengatasi SentiWordNet secara bersendirian
dengan ketepatan 30%.
TABLE OF CONTENTS
CHAPTER TITLE PAGE
DECLARATION ii
DEDICATION iii
ACKNOWLEDGEMENT iv
ABSTRACT v
ABSTRAK vi
TABLE OF CONTENTS vii
LIST OF TABLES xiii
LIST OF FIGURES xv
LIST OF ABBREVIATION xvi
LIST OF APPENDENCES xvii
1 INTRODUCTION 1
1.1 Introduction 1
1.2 Problem Background 4
1.3 The Problem Statement 6
1.4 Research Objectives 8
1.5 Research Questions 8
1.6 Scope of Study 8
1.7 Significance and Contributions of the Study 10
1.8 Structure of the Study 12
1.9 Chapter Summary 13
2 LITERATURE REVIEW 15
2.1 Background 15
Representation – A survey of Previous Works 19
2.3.1 Item ExtractionTechniques 20
2.3.2 Aspect-Level Extraction Techniques 22 2.3.3 Opinion Knowledge Representation 28 2.3.4 Discussion on Opinion Extraction and
Representation 30
2.3.5 Sentiment Polarity Classification and
Summarization 33
2.3.6 Strength of Sentiments (Senti Strength) 36
2.3.7 Summarization Techniques 39
2.3.8 Discussion on Sentiment Polarity Classification
and Summarization 42
2.4 Opinion Mining Lexical Resources and Databases 45 2.4.1 WordNet and WordNet Effect 46
2.4.2 HowNet 46
2.4.3 ConceptNet 47
2.4.4 Amazon Mechanical Turk (AMT) 47
2.4.5 SenticNet 48
2.4.6 SentiWordNet 49
2.4.7 Why SentiWordNet? 50
2.5 Opinion Mining Domain Taxonomy 51
2.6 Building Datasets and Corpus for Opinion Mining 54
2.7 Fuzzy Logic 58
2.7.1 Fuzzy Logic System 59
2.7.2. Research on Fuzzy Logic 60 2.7.3 Challenges for Fuzzy-based approaches 64
2.7.4 Why Fuzzy Logic? 65
2.8 Chapter Summary 67
3 RESEARCH METHODOLOGY 73
3.1 Introduction 73
3.2 Research Operational Framework 74
3.2.2 Phase II: Experiment Setup 78 3.2.2.1 Knowledge Acquisition and Dataset
Preparation for Domain Taxonomy 79 3.2.2.2 Algorithm Development 82 3.2.2.3 Overall Solution Design 87 3.2.2.4 Questionnaire 87
3.2.3 Phase III: Enhancing Aspect Level Opinion Mining Knowledge Extraction and
Representation by using Opinion Predicates and opinion Senti Strength for Adjectives,
Verbs, Adverbs and Nouns (AVAN) 88 3.2.3.1 Aspect-based Approach 88 3.2.3.2 Expert Opinions on Proposed
Opinion Levels and Value Ranges 90 3.2.3.3 Proposing AVAN Structure 93 3.2.3.4 Building Opinion Predicate and
Opinion Accounting 96 3.2.4 Enhancing Opinion Scoring by Using
SentiWordNet and Opinion Senti Strengths 100 3.2.4.1 Study SentiWordNet structure and
Scoring Methods 100
3.2.4.2 Define Scoring Formulas for
AVAN Components 101
3.2.4.3 Enhancing AVAN scoring further 101 3.2.4.4 Define Opinion Senti Strengths 102 3.2.4.5 Enhance scoring for AVAN using
Opinion Senti Strengths 103 3.2.5 Phase IV – Enriching Aspect level Opinion
Mining Representation by Using Fuzzy
Logic 104
3.2.5.1 Fuzzy Sets and Logic 105 3.2.6 Stages of Opinion Score Enhancements 108
4 ENHANCING ASPECT LEVEL OPINION MINING KNOWLEDGE EXTRACTION AND
REPRESENTATION BY USING OPINION
PREDICATES AND OPINION SENTI STRENGTH FOR ADJECTIVES, VERBS, ADVERBS AND NOUNS
(AVAN) 112
4.1 Introduction 112
4.2 The AVAN Approach 113
4.3 Extraction of Opinion, Aspect and Senti Strength Words 113
4.4 Building Opinion Predicates 115
4.4.1 Opinion Predicates – Development Stages 117 4.4.2 Building Opinion Predicates – An Example 118
4.5 Building Opinion Accounting 121
4.6 Enhancing Opinion Scoring By SentiWordNet and
Opinion Senti Strengths 124
4.6.1 SentiWordNet 125
4.6.2 The Proposed Approach 129 4.6.3 Opinion Senti Strengths 131 4.6.4 Enhancing Opinion Scoring 133 4.6.4.1 Adjective Scoring 134
4.6.4.2 Verb Scoring 136
4.6.4.3 Adverb Scoring 138
4.6.4.4 Noun Scoring 139
4.6.5 Opinion Senti Strength Scoring 140 4.6.6 Overall Opinion Strength 143 4.6.7 AVAN Scoring Algorithm 147
4.7 Applying the Proposed Approach 149
4.7.1 Opinion Extraction and Score Calculations 149 4.7.2 Opinion Accounting Calculation 157 4.7.3 Discussion and Analysis 159 4.8 Grouping Aspects into Categories 160
4.9 Results and Scoring Analysis 163
5 ENRICHING ASPECT LEVEL OPINION MINING
REPRESENTATION BY USING FUZZY LOGIC 168
5.1 Introduction 168
5.2 Fuzzy Logic Components 169
5.3 Fuzzy Logic and Experiment setup 171 5.3.1 The Proposed Fuzzy Algorithm 171 5.3.2 Fuzzy Sets, Membership Functions and Fuzzy
Rules 172
5.3.2.1 Positive and Negative Sets 173 5.3.2.2 Opinion Senti Strength Sets 174
5.3.2.3 Fuzzy Knowledgebase 175
5.3.2.4 Fuzzy Rules 178
5.4 The Detailed Fuzzy Process 179
5.4.1 A Trail Run of Fuzzy Process 182
5.4.1.1 Fuzzy Sets 182
5.4.1.2 Fuzzification 184
5.4.1.3 Fuzzy Rules 184
5.4.1.4 Defuzzification 185
5.5 Score Comparison 186
5.6 Analyzing the Fuzzy Logic Approach 189
5.7 Chapter Summary 191
6 CLASSIFICATION RESULTS 192
6.1 Introduction 192
6.2 Dataset 192
6.3 Experiment Setup 193
6.4 Results and Analysis 194
6.5 Benchmarking 199
6.6 Conclusion 201
7 SUMMARY AND CONCLUDING REMARKS 202
7.1 Summary 202
7.3 Enhancements / Limitations of This Study 208 7.4 Future Enhancements and Open Research Areas 208
REFERENCES 212
LIST OF TABLES
TABLE NO. TITLE PAGE
2.1 Corpus and Data Sets in an Alphabetical Order (Pang and
Lee 2008) 56
3.1 Research Phases and Activities 75
3.2 Result of Expert Opinions 91
3.3 Opinion Levels 106
3.4 Fuzzy Knowledgebase 107
4.1 Record Structure of SentiWordNet Database 127
4.2 Sample SentiWordNet Data 127
4.3 Score Statistics Per Part of Speech (Esuli et al., 2006) 128
4.4 AVAN Scoring Formulas 144
4.5 Analysis of On-line Passenger Reviews (PARA-1) 150 4.6 Analysis of On-line Passenger Reviews (PARA-2) 152 4.7 Analysis of On-line Passenger Reviews (PARA-3) 155
4.8 Opinion Accounting (ALL PARAs) 157
4.9 Aspect Categorization for Oman Air In-flight Items and
Services 162
5.1 Fuzzy Knowledgebase 176
5.2 Scores of Opinion Words Using SentiWordNet, AVAN
6.1 Performance of Selected Classifiers 194 6.2 Class-wise Performance Using All Selected Classifiers 196 6.3 Class-wise Overall Performance Using All Selected
Classifiers
196
6.4 Comparison Among Selected Classification Features
Using SMO 197
6.5 Performance Comparison (Jorge’s Vs.
LIST OF FIGURES
FIGURE NO. TITLE PAGE
2.1 Fuzzy Logic System 60
3.1 Research Operational Framework 74
3.2 Algorithm – Oman Air Corpus 85
3.3 Process for a Fuzzy-based System (Mamdani 1977) 105 4.1 Algorithm for Extracting Opinion Words and Predicates 114 4.2 Position of a Term in SentiWordNet (Esuli 2005) 125 4.3 Overall Extraction and Representation Process 131 4.4 AVAN Scoring and Opinion Accounting Algorithm 148
5.1 Fuzzy Logic Overall Structure 169
5.2 Fuzzy Logic Scoring Algorithm 172
5.3 Positive-Negative (PN) Membership Graph 173
5.4 Opinion Senti Strength (D) Graph 175
5.5 Opinion (OP) Graph 177
5.6 Positive-Negative (PN) Graph (X = 55 & Y = 1) 183 5.7 Positive-Negative Graph (X = 15 & Y = 0.82) 183 5.8 The Opinion Graph (Crisp value of 63 for “Less Clear”) 185 6.1 Classifiers’ Precision, Recall and F-Measure 194
6.2 Classifier-wise Accuracies 195
6.3 Accuracy of Classification Features Using SMO 198 6.4 Accuracy of Different Combination of
Classification Features Using SMO 198
6.5 Accuracy of SentiWordNet with Individual
LIST OF ABBREVIATIONS
AVAN - Adjectives, Verbs, Adverbs, Nouns CBA - Classification Based on Association
FL - Fuzzy Logic
OM - Opinion Mining
POS - Part of Speech
LIST OF APPENDICES
APPENDIX TITLE PAGE
A Review Of Extraction, Polarity And Summerization Tools,
Techniques And Methods 229
B Sample Reviews About Oman Air Services 245
C Oman Air On-Board Item And Service Aspects (Data
Dictionary) 251
CHAPTER 1
INTRODUCTION
1.1 Introduction
With the advent of Web 2.0, many new technologies and platforms have emerged such as blogs, discussion forums, e-commerce sites to enable people procure products and services and provide their opinions and feedbacks online. Consumers have at their disposal different types of information on the web which enable them to share their experiences and opinions (positive or negative) on any product or service (Zabin and Jefferies 2008). Different people often express their experience, opinions and thoughts on almost anything at different occasions and places. One person may find a particular feature is interesting; whereas, it may not make sense for another.
triggered the need to enhance existing methods and techniques to extract and summarize opinions of different online reviews (Pang and Lee, 2004).
Opinion mining is a field that offers a number of tools and techniques that are used to find people / customers’ opinions on certain products, services, events, occasions etc. The mining process can be as simple as learning polarity (positive or negative) and sentiment of the words, or as complicated as performing deep parsing of data to identify grammar and structure of the sentences. Opinion mining seeks extracting useful information from the opinionated sentences written in different forums, articles, books, product reviews etc and then presenting such details in textual summaries or visual presentations for quick reference and decision making.
Opinion mining is an important field as it helps to achieve the following objectives (Pang and Lee 2008):
To understand customers’ feelings and opinions on a particular product/service in order to improve the quality and delivery of such goods and as expressed in everyday communications - this will in turn help to enhance products and services.
To scientifically record different opinions and positions of people and various parties on a specific event, accident, incident, occasion etc. This will in turn help to put proper measures on how to handle such cases based on people opinions.
To improve social services provided to public by governments and social organizations by understanding their demands and suggestions.
Getting people expressed opinions on goods and services.
Companies, supplier and manufacturer firms can utilize on line reviews to respond to their consumer insights by modifying their marketing messages, brand positioning, product development and other activities accordingly (Zabin and Jefferies 2008).
The rise of many machine learning methods in natural language processing (NLP) and information retrieval (IR);
The datasets availability for machine learning algorithms to be trained on, due to the blossoming of the World Wide Web and, specifically, the development of review-aggregation web-sites.
Awareness of intellectual challenges and commercial and intelligence applications that the area offers.
English opinionated sentences can be Positive, Negative or Neutral. The following are few sentences which resemble few challenges:
Jane Austen’s books madden me so that I can’t conceal my frenzy from
reader
=> Positive
The Power puff girls learned that with great power comes great responsibility
=> Neutral
At movies gonna watch the mechanic, hope this thing is good
=> Neutral
I don’t think I’ve seen one Adam Sandler movie that’s not good
=> Positive
If I don’t see Source Code this weekend it will have been a complete waste.
=> Positive
The battery life of this laptop is very very low
=> Negative
also affected by the presence of linguistic hedges such as modifiers (e.g., “not”), concentrators (e.g., “very,” “extremely”), and dilators (e.g., “quite,” “almost,” and “nearly”). Zadeh developed the concept of fuzzy linguistic variables and linguistic hedges that modify the meaning and intensity of their operands (Huynh et al., 2002). Recent papers in this field have also pointed out that the task of opinion mining is sensitive to such hedges and taking the effect of linguistic hedges into consideration can improve the efficiency of the sentiment classification task (Dalal and Zaveri 2013).
1.2 Problem Background
Opinions of people about a specific subject, product or service require effective techniques and methods in order to extract, classify, represent and summarize them for better decision making (Haji 2009; Dan 2010; Ayesha et al., 2012; Jayashri. and Mayura, K. 2013). There are still rich research areas which are not well addressed by scholars in this field (Haji 2009; Yongyong et al., 2010; Bjørkelund et al., 2012; Moraes et al., 2013). Among these areas are the following:
From the existing literature on opinion mining, one can easily notice that the focus is more on adverbs and adjectives (Farah et. al., 2007; Pang and Lee 2009; Hana 2011; Moraes et al., 2013). Other part of speech like verbs and Nouns are not widely addressed and especially when analyzing and processing opinion scores. Opinion words can be adjectives, adverbs, verbs and nouns and analysis of such combination has not been addressed till date of this research. Addressing such a combination to enhance opinion classifications, scoring and summarization is still an important problem which needs to be addressed (Bjørkelund et al., 2012; Moraes et al., 2013).
“opinion object”, “opinion creation date”, “opinion polarity” and “detailed opinions”). Both of these representations do not represent all characteristics of an opinion. Elements like product/service features, opinion score, intensity of an opinion, group to which product feature belongs are missing and need to be considered while representing an extracted opinion. Moreover, better representation structures need to be designed to enable further processing of extracted opinions. Existing structures can be improved by adding the above suggested elements or characteristics of an opinion.
Moreover, in order to properly identify or measure the strength of an opinion, it is important to convert such an opinion to a value (called a score) which represent its strength. For example, the opinion word ‘beautiful’ can be given a score of 85 to show that it is a highly positive opinion. Existing studies on scoring opinion words, sentences and documents are still in its infancy and needs to be well researched. There are many permutations and options to be considered and studied while analyzing in depth the area of opinion scoring (Elomaa et al., 2011). SentiWordNet, which is a widely used lexical resource in the field of opinion mining, provides scores to various subjective words. However, such scoring method is a basic one and in many cases the assigned scores do not reflect the actual meaning and level of opinion words. For example the score for the word “large” is 0.50. This score does not really reflect the meaning of this adjective. There is a need to enhance such scorings by introducing better formulas to obtain scores that reflect the meaning of the subjective word.
score of 85 is assigned to the opinion word “beautiful,” a higher score (like 90) needs to be assigned to the opinion “very beautiful”. Moreover, the opinion “extremely beautiful” needs to be assigned much higher score. Such opinion strength and assignment of strength score are not well addressed in the literature and this is one of the major areas which needs to be studied.
Since opinions are fuzzy in nature, Fuzzy logic is not properly used to enhance the opinion mining field as explained in chapter 2. Since opinion words are vague, fuzzy logic is a well-known tool to address vagueness in opinion words for better sentiment analysis. Only very few studies done by Samaneh et al. (2010) and Animesh et al. (2011). Animesh did not utilize features of fuzzy logic covering fuzzification, fuzzy rules and defuzzification process. These are very important phases of fuzzy logic and implementing them can enhance the scoring of opinion words. Samaneh on the other side did define membership functions for positive, negative and opinion intensities. Values are predefined based on the classification module which was defined at the beginning. Moreover, fuzzy rules were based on linguistic patterns which were predefined patterns and these cannot be assured to be comprehensive to cover all cases in reviews. In Addition, the defuzzification crisp values are also predefined using a set of expected results. These needs to be addressed and full features of fuzzy calculus need to be applied to produce better crisp opinion values or scores.
1.3 The Problem Statement
In view of the problem background and based on previous approaches, it can be concluded that the following major problems are encountered and need to be further improved:
powerful techniques for the betterment of opinion mining and sentiment analysis.
The lack of proper representation of extracted opinions for better scoring and summarization.
Existing scoring methods are dependent on SentiWordNet with limited improvement on the usage of scoring formulas.
Opinion intensity, degree or strength of expressed sentiment is judged on the usage of adverb words. Opinions can be expressed at different senti strengths and levels by using words other than adverbs and such senti strengths are not properly measured.
Fuzzy logic is an important tool which is developed to address vague and unclear problems. Hence, this tool can be used to enhance opinion representation by placing the produced fuzzy crisp values in opinion predicates.
Hence, this research will focus on resolving the above problems by addressing the below queries and issues.
Can other types of part of speech apart from adjectives and adverbs be used to enhance opinion mining and opinion scoring especially if clumped with tools like SentiWordNet and other effective techniques?
How can knowledge extraction of opinions be enhanced?
How can representation of extracted opinions be improved?
Can extracted opinions be represented in vectors called predicates?
How can the strength of an opinion be extracted and represented?
How can summarization of opinions be enhanced?
How can SentiWordNet be used to score opinion words and opinion strengths?
1.4 Research Objectives
This research is intended to meet the following key objectives:
To enhance aspect level opinion mining knowledge extraction by introducing opinion predicates for Adjective, Verb, Adverbs and Nouns (AVAN).
To enhance aspect level opinion mining knowledge representation by introducing opinion Senti Strength.
To enrich aspect level opinion mining representation by using fuzzy logic.
1.5 Research Questions
This study will answer the following questions:
Can aspect level opinion mining knowledge extraction be enhanced by introducing opinion predicates for Adjectives, Verbs, Adverbs and Nouns (AVAN)?
How can aspect level opinion mining knowledge representation be enhanced by introducing opinion senti strength?
How can aspect level opinion mining representation be enriched by using fuzzy logic?
1.6 Scope of the Study
Sentiment Analysis can be done on document, sentence and aspect levels. This study focuses on aspect level opinion mining. The main focus of this study is to enhance the extraction and representation of opinion mining knowledge on aspect level only. This thesis does not address sentiment analysis on document and sentence levels.
This study handles explicit opinions only and does not deal with other types of opinions like hidden, emotion, implicit, spam and sarcastic opinions. Hidden opinions are opinions that are not explicitly stated in the sentence like “I have stayed in this hotel for more than 10 times.” This implies that this person likes this hotel but it is not explicitly stated. Spam opinions are fake opinions that are made about a product or service for the purpose of broadcasting false news. Sarcastic opinions are expressed without using sentiment words and are expressed in an opposite manner like “What a great car! It stopped working in two days.” The previous example also expresses an emotion opinion which is also not covered in this thesis. Emotion opinions are opinions that are expressed with emotions and which are not explicitly stated. (Bing Liu 2012).
In addition to the above, this thesis does not address context awareness sentiment analysis. Here an opinion word meaning can change based on the context in which it is used. One can use a word and can be viewed as positive in one situation. However, if the same word is used in another context, it can be viewed as negative. For example, the word ‘long’ can be positive if it is used for a battery life of a mobile phone. On the other side, the word ‘long’ can indicate a negative meaning if it used for a process that takes long time. In many cases, it is difficult to distinguish between the two contexts. Another challenge is that in few cases opinion words also occur in objective sentences like “it is a long distance between Florida and California.” Here the word ‘long’ is used in a factual sentence. This thesis does not cover the “context awareness” of used opinions in text.
In the first part, the thesis looks at the opinion mining aspect through extraction and representation of data into opinion predicates and then such representation is enhanced by producing opinion scoring and accounting. Secondly the fuzzy logic analysis will be used as a supportive method to enhance the representation of opinions by determining the polarity and strength of these opinions.
In addition to the above, this study uses SentiWordNet as a lexical resource in order to enhance the extraction, representation and scoring of opinions. This is due to the following reasons (Esuli and Sebastiani 2006; Elomaa et al., 2011; Kennedy et al., 2002; Burns et al., 1990).
SentiWordNet is built specifically for the field of opinion mining and sentiment analysis. Hence, it is a powerful resource for extraction, representation and scoring of opinions.
SentiWordNet is built on the well-know lexical resource WorldNet which groups words into sets of synonyms named as synsets and it records all relations among these synonym sets or their members. (Pang and Lee 2008).
SentiWordNet is widely used in opinion mining literature for opinion representation, classification and scoring. Hence, it will be more professional and accurate to compare work of this thesis with previous works when using SentiWordNet.
SentiWordNet provides three score for each word or synset (Positive, Negative and Objective). These scores help a lot in analyzing and classifying sentiments easily.
All other existing lexical resources like HowNet, ConceptNet, SenticNet, WordNet and other dictionaries lack the above characteristics.
1.7 Significance and Contributions of the Study
The main contribution of this study is the enhanced way to represent aspect level opinion mining knowledge using a structure called ‘Opinion Predicate.’ A predicate is a vector space consisting of the main components of an opinion in an ordered way. Such a rich structure will empower opinion processing, analysis and scoring. Few of the previous studies represented opinions in tuples which hold very basic elements of an opinion. This study introduces an opinion predicate structure which covers important elements (like opinion strength, score, opinion aspect, aspect group) of an opinion and these elements were not covered in previous representations.
This is the first study to introduce the combination of AVAN (Adjectives, Verb, Adverb and Noun) for extracting, classifying, scoring and summarizing extracted opinion words by using SentiWordNet, Opinion senti strength and fuzzy logic tools and techniques. Most of the previous studies focused on Adjectives and Adverbs as the main source for opinion words. Nouns and verbs can also be used as opinion words and considered by few previous studies. The reasons for selecting SentiWordNet are highlighted above and in chapter two after analyzing various opinion mining lexical resources and databases.
This study introduces opinion strength or degree to properly capture the strength of an opinion (this is referred in the literature as ‘Senti Strength.’). No studies as of the date of this thesis introduced opinion strength for all part of speech opinion words (AVAN) which is an important contribution of this research. Previous studies used only adverb as a word which can intensify opinion adjectives.
Using fuzzy logic is another major contribution to further enhance the representation of opinion by adding more accurate score in the opinion predicate. This study has utilized the power of fuzzy logic to analyses in-depth opinion words. The reasons behind using fuzzy logic are given in section 2.7. All the three major phases of fuzzy logic (Fuzzification, Fuzzy Rules and Defuzzification) are implemented. In addition, this research demonstrates the importance of an integrative use of NLP and fuzzy logic as a basis for modeling abstract relationship. None of the existing studies has done such multi-level analysis using fuzzy logic the way applied in this thesis.
1.8 Structure of the Study
The thesis is placed out in seven chapters, as follows
The first chapter introduces the reader to the concept of opinion mining and describes the aims and objectives of the study. It also indicates the scope of the research and sheds the light on the contributions of this study.
The actual study will begin from chapter two by reviewing the existing literature in this field. The second chapter mainly analyzes in depth existing approaches, methods, techniques and challenges found in the area of sentiment analysis.
Chapter three reviews and explains the stages of this research and the methodology which is adopted to effectively complete this research. It also highlights various techniques that will be used to meet the set objectives. All definitions and experiment setups are explained in this chapter.
SentiWordNet, its structure and its scoring norms. In addition, this chapter explains AVAN scoring formulas and the enfacements made to effectively score opinion words using Opinion senti strength.
Chapter five explains how aspect level opinion mining knowledge representation can be enriched by using fuzzy logic. All stages of the fuzzy process are explained with examples to illustrate these concepts.
Chapter six gives detail results of classifying customer reviews using SentiWordNet, AVAN, Opinion senti Strength and fuzzy logic as classification features using SMO classifier.
Chapter seven summarizes the thesis, highlights all the contributions and lists major open issues and challenges that need to be addressed in future researches.
1.9 Chapter Summary
Opinion mining aims to track and summarize opinions of public about a product or a service. Research on opinion mining is a vast area covering many aspects among which is how to properly extract, classify, represent and score opinion strengths in order to take proper decisions. Opinion mining systems should study the degrees and strengths of opinions rather than classifying them as either zero or one. Existing studies on scoring opinion words, sentences and documents still have long way to go. There are many rich areas that need to be addressed and solved in order to scientifically present proper opinions to both service/product providers and customers. This study embarked based on such needs and based on many open areas in this rich field of research.
people may like few features and dislikes others and hence polarity analysis can be measured more accurately. Also aspect level allows capturing and better representing opinion together with sentiment strengths. The representation of aspect-level sentiments as predicates is the main contribution of this thesis. These predicates also capture sentiment strengths which can be exploited for various uses. To achieve this, powerful resources and tools like SentiWordNet and fuzzy logic are utilized in order to enhance the opinion knowledge extraction and representation. Furthermore, this thesis has proposed novel ideas and interesting directions for coupling fuzzy logic with an integrated use of NLP in representing and characterizing aspect-level sentiments.
In addition to the above and since this thesis focuses on aspect-level analysis of sentiments, there is clear connection between opinion mining and both SentiWordNet and fuzzy logic. SentiWordNet is a lexical resource or a database which contains all English words with positive and negative scores for each word. Hence, this resource can help a lot in analyzing opinions at aspect levels. On the other side, fuzzy logic is built to solve complex problems like handling vagueness of words based on defined classes, knowledgebase and fuzzy rules. Hence, fuzzy logic is an important tool to address opinion fuzzy nature. In view of the above, opinion mining, SentiWordNet and fuzzy logic create an excellent combination to enhance opinion extraction and representation at aspect level.
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