Table of Contents. Chapter No. 1 Introduction 1. iii. xiv. xviii. xix. Page No.

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1 Table of Contents Title Declaration by the Candidate Certificate of Supervisor Acknowledgement Abstract List of Figures List of Tables List of Abbreviations Chapter Chapter No. 1 Introduction 1 ii iii iv vi xiv xviii xix Page No. 1.1 Introduction Sentiment Analysis: Basic Concepts Sentiment and Subjectivity Classification Sentiment Classification at Document Level Sentiment Classification at Sentence Level Opinion Mining and Summarization Past Scenario of Collecting Opinions and their 25 Analysis Sentiment Analysis as Text Classification Problem Sentiment Analysis as Feature Classification Growth of Social Media and Its Impact on User 28 Demands Elements of Social Media Types of Data Generation in Social Media 34 Environment Requirements of using Social Media Contents in 35

3.2 Sentiment Classification at Sentence Level 22 1.3.3 Opinion Mining and Summarization 25 1.3.4 Past Scenario of Collecting Opinions and their 25 Analysis 1.3.5 Sentiment Analysis as Text Classification Problem 26 1.

2 Business Challenges to Handle Large Social Media Contents Reasons behind Sentiment Analysis of Social Media 39 Contents 1.5 Economic Consequences of Sentiment Analysis- 40 Impact on Individual, Society & Organization 1.6 Need for Automated Sentiment Analysis Current Status, Challenges in Sentiment Analysis Benefits of Automation of Opinion Analysis Problem on Hand Research Objectives Scope of Research Work Major Contributions of This Thesis Organization of Thesis 53 2 Review of Literature Introduction Literature Review on Opinion Mining or Sentiment 57 Analysis Concept 2.3 Review of Traditional Sentiment Analysis Research 60 Directions Text Classification Feature Selection Review of Opinion Mining or Sentiment Analysis 78 Systems 2.5 Treating Sentiment Analysis as Multi-Faceted Problem Problems with Opinion Mining Systems Data Mining in the Field of Opinion Mining: Study Introduction Relevant Data Mining Algorithms Requirement of Automated Sentiment Mining System Development 94

6.2 Benefits of Automation of Opinion Analysis 46 1.7 Problem on Hand 47 1.8 Research Objectives 50 1.9 Scope of Research Work 50 1.10 Major Contributions of This Thesis 53 1.

3 2.9 Findings of Literature Review 95 3 Role of Features in Sentiment Analysis Introduction Features Words and Stems Details of Term Frequency Occurrences and Inverse 103 Document Frequency Occurrences Binary versus Term Frequency Occurrences Based on 105 Weights Negations Feature Selection Techniques Frequency Based Selection Part of Speech Based Selection Lexicon-Based Selection Feature-Based Sentiment Analysis Feature Extraction Conclusion Summary of Findings Design of Automated Sentiment Discovery System 4.1 Introduction Reason behind Automation in Sentiment Analysis Architectural Design of Research Work Theoretical Formulation Mathematical Formulation Algorithms Used to Build System Model Generation of Mathematical Model based on 138 Set Theory 4.4 Modeling of Proposed Research Work as System Proposed System

3.3 Lexicon-Based Selection 113 3.4 Feature-Based Sentiment Analysis 114 3.5 Feature Extraction 116 3.6 Conclusion 120 3.7 Summary of Findings 121 4 Design of Automated Sentiment Discovery System 4.

4 4.4.2 Data Collection and Preprocessing Opinion Mining Engine Opinion Status Conclusion Implementation of Automated Sentiment Discovery System 5.1 Introduction System Requirements Identified Software Used for System Development Use of Python (Programming Language) Use of WordNet Use of Oracle Database Use of NLTK Use of WEKA Version Use of StarUML (Open Source UML/MDA Platform) 170 version Module wise Implementation Data Collection and Data Preprocessing Feature Extraction Phase Opinion Orientation Engine Generation of Opinion Status Discussion Summary Performance Evaluation of Automated Sentiment or Opinion Discovery System 6.1 Introduction Evaluation Parameters for OMS Evaluation Test Setup Data Set

3.5 Use of WEKA Version 3.6 169 5.3.6 Use of StarUML (Open Source UML/MDA Platform) 170 version 5.0.2.1570 5.4 Module wise Implementation 170 5.4.1 Data Collection and Data Preprocessing 171 5.4.2 Feature Extraction Phase 178 5.

5 6.5 Usefulness of Amazon Review Dataset Introduction Criticism against Amazon Review Data Set Supporting Facts of Amazon Review Data Set Data Collection and Data Preprocessing Analysis of 202 Test Data 6.7 Opinion Mining Result Analysis for FBS and OB 207 Systems 6.8 Performance Analysis of the Proposed Research Work Introduction Performance Analysis for developed Automated 218 Sentiment System 6.9 Results based F-Score Performance Measure Comparative Evaluation of Research Work with FBS and OB OMS Summary and Conclusion Summary and Conclusion Recommendations Future Scope Limitations of Research Work 267 Appendix A: Publications Bibliography

8 Performance Analysis of the Proposed Research Work 217 6.8.1 Introduction 217 6.8.2 Performance Analysis for developed Automated 218 Sentiment System 6.

Bing Liu. Web Data Mining. Exploring Hyperlinks, Contents, and Usage Data. With 177 Figures. ~ Spring~r

Bing Liu. Web Data Mining. Exploring Hyperlinks, Contents, and Usage Data. With 177 Figures. ~ Spring~r Bing Liu Web Data Mining Exploring Hyperlinks, Contents, and Usage Data With 177 Figures ~ Spring~r Table of Contents 1. Introduction.. 1 1.1. What is the World Wide Web? 1 1.2. ABrief History of the Web