Sentiment Analysis
Mining Opinions, Sentiments, and Emotions

A comprehensive introduction to computational analysis of sentiments, opinions, emotions, and moods. Now including deep learning methods.

Bing Liu (Author)

9781108486378, Cambridge University Press

Hardback, published 15 October 2020

448 pages
24 x 15.9 x 2.7 cm, 0.78 kg

'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

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.

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.

Subject Areas: Machine learning [UYQM], Natural language & machine translation [UYQL], Information retrieval [UNH], Data mining [UNF], Advertising [KJSA], Political campaigning & advertising [JPVL], Psychology: emotions [JMQ], Advertising & society [JFDV], Research methods: general [GPS], Computational linguistics [CFX]