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Sentiment Analysis and Opinion Mining

Book Description

Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis. Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities and are key influencers of our behaviors. Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. This is true not only for individuals but also for organizations. This book is a comprehensive introductory and survey text. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily use it in class for courses on natural language processing, social media analysis, text mining, and data mining. Lecture slides are also available online. Table of Contents: Preface / Sentiment Analysis: A Fascinating Problem / The Problem of Sentiment Analysis / Document Sentiment Classification / Sentence Subjectivity and Sentiment Classification / Aspect-Based Sentiment Analysis / Sentiment Lexicon Generation / Opinion Summarization / Analysis of Comparative Opinions / Opinion Search and Retrieval / Opinion Spam Detection / Quality of Reviews / Concluding Remarks / Bibliography / Author Biography

Table of Contents

  1. Cover Page
  2. Title Page
  3. Copyright Page
  4. Abstract
  5. Acknowledgments
  6. Contents
  7. Preface
  8. 1. Sentiment Analysis: A Fascinating Problem
    1. 1.1 Sentiment Analysis Applications
    2. 1.2 Sentiment Analysis Research
      1. 1.2.1. Different Levels of Analysis
      2. 1.2.2. Sentiment Lexicon and its Issues
      3. 1.2.3. Natural Language Processing Issues
    3. 1.3 Opinion Spam Detection
    4. 1.4 What's Ahead
  9. 2. The Problem of Sentiment Analysis
    1. 2.1 Problem Definitions
      1. 2.1.1. Opinion Defintion
      2. 2.1.2. Sentiment Analysis Tasks
    2. 2.2 Opinion Summarization
    3. 2.3 Different Types of Opinions
      1. 2.3.1. Regular and Comparative Opinions
      2. 2.3.2. Explicit and Implicit Opinions
    4. 2.4 Subjectivity and Emotion
    5. 2.5 Author and Reader Standpoint
    6. 2.6 Summary
  10. 3. Document Sentiment Classification
    1. 3.1 Sentiment Classification Using Supervised Learning
    2. 3.2 Sentiment Classification Using Unsupervised Learning
    3. 3.3 Sentiment Rating Prediction
    4. 3.4 Cross-Domain Sentiment Classification
    5. 3.5 Cross-Language Sentiment Classification
    6. 3.6 Summary
  11. 4. Sentence Subjectivity and Sentiment Classification
    1. 4.1 Subjectivity Classification
    2. 4.2 Sentence Sentiment Classification
    3. 4.3 Dealing with Conditional Sentences
    4. 4.4 Dealing with Sarcastic Sentences
    5. 4.5 Cross-Language Subjectivity and Sentiment Classification
    6. 4.6 Using Discourse Information for Sentiment Classification
    7. 4.7 Summary
  12. 5. Aspect-Based Sentiment Analysis
    1. 5.1 Aspect Sentiment Classification
    2. 5.2 Basic Rules of Opinions and Compositional Semantics
    3. 5.3 Aspect Extraction
      1. 5.3.1. Finding Frequent Nouns and Noun Phrases
      2. 5.3.2. Using Opinion and Target Relations
      3. 5.3.3. Using Supervised Learning
      4. 5.3.4. Using Topic Models
      5. 5.3.5. Mapping Implicit Aspects
    4. 5.4 Identifying Resource Usage Aspect
    5. 5.5 Simutaneous Opinion Lexicon Expansion and Aspect Extraction
    6. 5.6 Grouping Aspects into Categories
    7. 5.7 Entity, Opinion Holder, and Time Extraction
    8. 5.8 Coreference Resolution and Word Sense Disambiguation
    9. 5.9 Summary
  13. 6. Sentiment Lexicon Generation
    1. 6.1 Dictionary-Based Approach
    2. 6.2 Corpus-Based Approach
    3. 6.3 Desirable and Undesirable Facts
    4. 6.4 Summary
  14. 7. Opinion Summarization
    1. 7.1 Aspect-Based Opinion Summarization
    2. 7.2 Improvements to Aspect-Based Opinion Summarization
    3. 7.3 Contrastive View Summarization
    4. 7.4 Traditional Summarization
    5. 7.5 Summary
  15. 8. Analysis of Comparative Opinions
    1. 8.1 Problem Definitions
    2. 8.2 Identify Comparative Sentences
    3. 8.3 Identifying Preferred Entities
    4. 8.4 Summary
  16. 9. Opinion Search and Retrieval
    1. 9.1 Web Search vs. Opinion Search
    2. 9.2 Existing Opinion Retrieval Techniques
    3. 9.3 Summary
  17. 10. Opinion Spam Detection
    1. 10.1 Types of Spam and Spamming
      1. 10.1.1. Harmful Fake Reviews
      2. 10.1.2. Individual and Group Spamming
      3. 10.1.3. Types of Data, Features, and Detection
    2. 10.2 Supervised Spam Detection
    3. 10.3 Unsupervised Spam Detection
      1. 10.3.1. Spam Detection Based on Atypical Behaviors
      2. 10.3.2. Spam Detection Using Review Graph
    4. 10.4 Group Spam Detection
    5. 10.5 Summary
  18. 11. Quality of Reviews
    1. 11.1 Quality as Regression Problem
    2. 11.2 Other Methods
    3. 11.3 Summary
  19. 12. Concluding Remarks
  20. Bibliography
  21. Author Biography