Bing Liu

Sentiment Analysis (eBook, PDF)

Mining Opinions, Sentiments, and Emotions

• Format: PDF

Produktbeschreibung

Sentiment analysis is the computational study of people''s opinions, sentiments, emotions, moods, and attitudes. This fascinating problem offers numerous research challenges, but promises insight useful to anyone interested in opinion analysis and social media analysis. This comprehensive introduction to the topic takes a natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs commonly used to express opinions, sentiments, and emotions. The book covers core areas of sentiment analysis and also includes related topics such as debate analysis, intention mining, and fake-opinion detection. It will be a valuable resource for researchers and practitioners in natural language processing, computer science, management sciences, and the social sciences. In addition to traditional computational methods, this second edition includes recent deep learning methods to analyze and summarize sentiments and opinions, and also new material on emotion and mood analysis techniques, emotion-enhanced dialogues, and multimodal emotion analysis.

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Produktdetails

• Verlag: Cambridge University Press
• Erscheinungstermin: 15. Oktober 2020
• Englisch
• ISBN-13: 9781108788212
• Artikelnr.: 70913091

Autorenporträt

Bing Liu is a distinguished professor of Computer Science at the University of Illinois at Chicago. His current research interests include sentiment analysis, lifelong machine learning, natural language processing, and data mining. He has published extensively in top conferences and journals, and his research has been cited on the front page of the New York Times. Three of his research papers also received Test-of-Time awards. He is the recipient of ACM SIGKDD Innovation Award in 2018, and is a Fellow of the ACM, AAAI, and IEEE. He served as the Chair of ACM SIGKDD from 2013-2017.

Inhaltsangabe

1. Introduction
2. The Problem of Sentiment Analysis
3. Document Sentiment Classification
4. Sentence Subjectivity and Sentiment Classification
5. Aspect Sentiment Classification
6. Aspect and Entity Extraction
7. Sentiment Lexicon Generation
8. Analysis of Comparative Opinions
9. Opinion Summarization and Search
10. Analysis of Debates and Comments
11. Mining Intents
12. Detecting Fake or Deceptive Opinions
13. Quality of Reviews
14. Conclusions.