A practical guide to sentiment analysis

• Other Authors: Cambria, Erik (Editor), Das, Dipankar (Editor), Bandyopadhyay, Sivaji, 1963- (Editor), Feraco, Antonio (Editor)
• Format: Electronic eBook
• Language: English
• Published: Cham : Springer, [2017]
• Series: Socio-affective computing ; v. 5.
• Subjects: Natural language processing (Computer science); Computational linguistics.; elektronické knihy; electronic books
• ISBN: 9783319553948; 9783319553924
• Physical Description: 1 online resource (vii, 196 pages)

This edited work presents studies and discussions that clarify the challenges and opportunities of sentiment analysis research. While sentiment analysis research has become very popular in the past ten years, most companies and researchers still approach it simply as a polarity detection problem. In...

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.