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Table of Contents
Preface; Contents; 1 Affective Computing and Sentiment Analysis; 1.1 Introduction; 1.2 Common Tasks; 1.3 General Categorization; 1.4 Conclusion; References; 2 Many Facets of Sentiment Analysis; 2.1 Definition of Opinion; 2.1.1 Opinion Definition; 2.1.2 Sentiment Target; 2.1.3 Sentiment and Its Intensity; 2.1.4 Opinion Definition Simplified; 2.1.5 Reason and Qualifier for Opinion; 2.1.6 Objective and Tasks of Sentiment Analysis; 2.2 Definition of Opinion Summary; 2.3 Affect, Emotion, and Mood; 2.3.1 Affect, Emotion, and Mood in Psychology; 2.3.2 Affect, Emotion, and Mood in Sentiment Analysis
2.4 Different Types of Opinions2.4.1 Regular and Comparative Opinions; 2.4.2 Subjective and Fact-Implied Opinions; 2.4.3 First-Person and Non-First-Person Opinions; 2.4.4 Meta-opinions; 2.5 Author and Reader Standpoint; 2.6 Summary; References; 3 Reflections on Sentiment/Opinion Analysis; 3.1 Introduction; 3.2 A Review of Current Sentiment Analysis; 3.3 The Needs and Goals Behind Sentiments; 3.3.1 Maslow's Hierarchy of Needs; 3.3.2 Finding Appropriate Goals for Actions and Entities; 3.4 Toward a Practical Computational Approach; 3.4.1 Examples and Illustration
3.4.2 A Computational Model of Each Part3.4.3 Prior/Default Knowledge About Opinion Holders; 3.5 Conclusion and Discussion; References; 4 Challenges in Sentiment Analysis; 4.1 Introduction; 4.2 The Array of Sentiment Analysis Tasks; 4.2.1 Sentiment at Different Text Granularities; 4.2.2 Detecting Sentiment of the Writer, Reader, and Other Entities; 4.2.3 Sentiment Towards Aspects of an Entity; 4.2.4 Stance Detection; 4.2.5 Detecting Semantic Roles of Feeling; 4.2.6 Detecting Affect and Emotions; 4.3 Sentiment of Words; 4.3.1 Manually Generated Term-Sentiment Association Lexicons
4.3.2 Automatically Generated Term-Sentiment Association Lexicons4.4 Sentiment of Phrases, Sentences, and Tweets: Sentiment Composition; 4.4.1 Negated Expressions; 4.4.2 Phrases with Degree Adverbs, Intensifiers, and Modals; 4.4.3 Sentiment of Sentences, Tweets, and SMS messages; 4.4.4 Sentiment in Figurative Expressions; 4.5 Challenges in Annotating for Sentiment; 4.6 Challenges in Multilingual Sentiment Analysis; 4.7 Challenges in Applying Sentiment Analysis; References; 5 Sentiment Resources: Lexicons and Datasets; 5.1 Introduction; 5.2 Labels; 5.2.1 Stand-Alone Labels; 5.2.2 Dimensions
5.2.3 Structures5.3 Lexicons; 5.3.1 Sentiment Lexicons; 5.3.1.1 SentiWordNet; 5.3.1.2 SO-CAL; 5.3.1.3 Sentiment Treebank & Associated Lexicon; 5.3.1.4 Summary; 5.3.2 Emotion Lexicons; 5.3.2.1 LIWC; 5.3.2.2 ANEW; 5.3.2.3 Emo-Lexicon; 5.3.2.4 WordNet-Affect; 5.3.2.5 Chinese Emotion Lexicon; 5.3.2.6 SenticNet; 5.3.2.7 Summary; 5.4 Sentiment-Annotated Datasets; 5.4.1 Sources of Data; 5.4.2 Obtaining Labels; 5.4.3 Popular Sentiment-Annotated Datasets; 5.5 Bridging the Language Gap; 5.6 Applications of Sentiment Resources; 5.7 Conclusion; References
2.4 Different Types of Opinions2.4.1 Regular and Comparative Opinions; 2.4.2 Subjective and Fact-Implied Opinions; 2.4.3 First-Person and Non-First-Person Opinions; 2.4.4 Meta-opinions; 2.5 Author and Reader Standpoint; 2.6 Summary; References; 3 Reflections on Sentiment/Opinion Analysis; 3.1 Introduction; 3.2 A Review of Current Sentiment Analysis; 3.3 The Needs and Goals Behind Sentiments; 3.3.1 Maslow's Hierarchy of Needs; 3.3.2 Finding Appropriate Goals for Actions and Entities; 3.4 Toward a Practical Computational Approach; 3.4.1 Examples and Illustration
3.4.2 A Computational Model of Each Part3.4.3 Prior/Default Knowledge About Opinion Holders; 3.5 Conclusion and Discussion; References; 4 Challenges in Sentiment Analysis; 4.1 Introduction; 4.2 The Array of Sentiment Analysis Tasks; 4.2.1 Sentiment at Different Text Granularities; 4.2.2 Detecting Sentiment of the Writer, Reader, and Other Entities; 4.2.3 Sentiment Towards Aspects of an Entity; 4.2.4 Stance Detection; 4.2.5 Detecting Semantic Roles of Feeling; 4.2.6 Detecting Affect and Emotions; 4.3 Sentiment of Words; 4.3.1 Manually Generated Term-Sentiment Association Lexicons
4.3.2 Automatically Generated Term-Sentiment Association Lexicons4.4 Sentiment of Phrases, Sentences, and Tweets: Sentiment Composition; 4.4.1 Negated Expressions; 4.4.2 Phrases with Degree Adverbs, Intensifiers, and Modals; 4.4.3 Sentiment of Sentences, Tweets, and SMS messages; 4.4.4 Sentiment in Figurative Expressions; 4.5 Challenges in Annotating for Sentiment; 4.6 Challenges in Multilingual Sentiment Analysis; 4.7 Challenges in Applying Sentiment Analysis; References; 5 Sentiment Resources: Lexicons and Datasets; 5.1 Introduction; 5.2 Labels; 5.2.1 Stand-Alone Labels; 5.2.2 Dimensions
5.2.3 Structures5.3 Lexicons; 5.3.1 Sentiment Lexicons; 5.3.1.1 SentiWordNet; 5.3.1.2 SO-CAL; 5.3.1.3 Sentiment Treebank & Associated Lexicon; 5.3.1.4 Summary; 5.3.2 Emotion Lexicons; 5.3.2.1 LIWC; 5.3.2.2 ANEW; 5.3.2.3 Emo-Lexicon; 5.3.2.4 WordNet-Affect; 5.3.2.5 Chinese Emotion Lexicon; 5.3.2.6 SenticNet; 5.3.2.7 Summary; 5.4 Sentiment-Annotated Datasets; 5.4.1 Sources of Data; 5.4.2 Obtaining Labels; 5.4.3 Popular Sentiment-Annotated Datasets; 5.5 Bridging the Language Gap; 5.6 Applications of Sentiment Resources; 5.7 Conclusion; References