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Sentiment Analysis

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Table of Contents

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 intentions; 12. Detecting fake or deceptive opinions; 13. Quality of reviews.

About the Author

Bing Liu is a Professor of Computer Science at the University of Illinois. His current research interests include sentiment analysis and opinion mining, data mining, machine learning, and natural language processing. He has published extensively in top conferences and journals, and his research has been cited on the front page of the New York Times. He is also the author of two books: Sentiment Analysis and Opinion Mining (2012) and Web Data Mining: Exploring Hyperlinks, Contents and Usage Data (1st edition, 2007; 2nd edition, 2011). He currently serves as the chair of ACM SIGKDD and is an IEEE Fellow.

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