Sentiment Analysis: Mining Opinions, Sentiments, and Emotions

by Bing Liu

Overview

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.

Product Details

ISBN-13:	9781108486378
Publisher:	Cambridge University Press
Publication date:	10/15/2020
Series:	Studies in Natural Language Processing
Edition description:	2nd ed.
Pages:	448
Sales rank:	681,333
Product dimensions:	6.26(w) x 9.45(h) x 1.06(d)

About the Author

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.

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 Intents; 12. Detecting Fake or Deceptive Opinions; 13. Quality of Reviews; 14. Conclusions.

Editorial Reviews

'As a whole, this book serves as a useful introduction to sentiment analysis along with in-depth discussions of linguistic phenomena related to sentiments, opinions, and emotions. Although many sentiment analysis methods are based on machine learning as in other NLP [Natural Language Processing] tasks, sentiment analysis is much more than just a classification or regression problem, because the natural language constructs used to express opinions, sentiments, and emotions are highly sophisticated, including sentiment shift, implicated expression, sarcasm, and so on. Liu has described these issues and problems very clearly. Readers will find this book to be inspiring and it will arouse their interests in sentiment analysis.' Jun Zhao, Chinese Academy of Sciences

From the Publisher