Sentiment Analysis and Opinion Mining

1. Front Matter

   Pages i-xiv

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2. Sentiment Analysis: A Fascinating Problem

   • Bing Liu

   Pages 1-8

3. The Problem of Sentiment Analysis

   • Bing Liu

   Pages 9-22

4. Document Sentiment Classification

   • Bing Liu

   Pages 23-36

5. Sentence Subjectivity and Sentiment Classification

   • Bing Liu

   Pages 37-48

6. Aspect-based Sentiment Analysis

   • Bing Liu

   Pages 49-77

7. Sentiment Lexicon Generation

   • Bing Liu

   Pages 79-89

8. Opinion Summarization

   • Bing Liu

   Pages 91-97

9. Analysis of Comparative Opinions

   • Bing Liu

   Pages 99-106

10. Opinion Search and Retrieval

    • Bing Liu

    Pages 107-111

11. Opinion Spam Detection

    • Bing Liu

    Pages 113-125

12. Quality of Reviews

    • Bing Liu

    Pages 127-131

13. Concluding Remarks

    • Bing Liu

    Pages 133-134

14. Back Matter

    Pages 135-167

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Sentiment analysis and opinion mining is the field of study that analyzes people's opinions, sentiments, evaluations, attitudes, and emotions from written language. It is one of the most active research areas in natural language processing and is also widely studied in data mining, Web mining, and text mining. In fact, this research has spread outside of computer science to the management sciences and social sciences due to its importance to business and society as a whole. The growing importance of sentiment analysis coincides with the growth of social media such as reviews, forum discussions, blogs, micro-blogs, Twitter, and social networks. For the first time in human history, we now have a huge volume of opinionated data recorded in digital form for analysis. Sentiment analysis systems are being applied in almost every business and social domain because opinions are central to almost all human activities and are key influencers of our behaviors. Our beliefs and perceptions of reality, and the choices we make, are largely conditioned on how others see and evaluate the world. For this reason, when we need to make a decision we often seek out the opinions of others. This is true not only for individuals but also for organizations. This book is a comprehensive introductory and survey text. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, researchers and practitioners who are interested in social media analysis in general and sentiment analysis in particular. Lecturers can readily use it in class for courses on natural language processing, social media analysis, text mining, and data mining. Lecture slides are also available online. Table of Contents: Preface / Sentiment Analysis: A Fascinating Problem / The Problem of Sentiment Analysis / Document Sentiment Classification / Sentence Subjectivity and Sentiment Classification / Aspect-Based Sentiment Analysis / Sentiment Lexicon Generation /Opinion Summarization / Analysis of Comparative Opinions / Opinion Search and Retrieval / Opinion Spam Detection / Quality of Reviews / Concluding Remarks / Bibliography / Author Biography

• University of Illinois at Chicago, USA

  Bing Liu

Bing Liu is a Distinguished Professor of Computer Science at the University of Illinois at Chicago. He received his Ph.D. in Artificial Intelligence from the University of Edinburgh. His research interests include lifelong machine learning, sentiment analysis and opinion mining, data mining, machine learning, and natural language processing. He has published extensively in top conferences and journals in these areas. Two of his papers have received 10-year Test-of-Time awards from KDD, the premier conference of data mining and data science. He has also authored three books: one on Web data mining and two on sentiment analysis. Some of his work has been widely reported in the popular press, including a front-page article in the New York Times. On professional services, he served as the Chair of ACM SIGKDD from 2013-2017, as program chair of many leading data mining related conferences, including KDD, ICDM, CIKM, WSDM, SDM, and PAKDD, as associate editor of many leading journals such asTKDE, TKDD, TWEB, and DMKD, and as area chair or senior PC member of numerous natural language processing, AI, Web research, and data mining conferences. He is a Fellow of the ACM, AAAI, and IEEE.

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