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Sentiment analysis: mining opinions, sentiments, and emotions (2nd ed.)
Liu B., Cambridge University Press, Cambridge, UK, 2020. 448 pp.  Type: Book (978-1-108486-37-8)
Date Reviewed: Oct 12 2021

Mining sentiment and opinions from human-generated reviews is certainly one of the most visible applications of natural language processing ever. It is hard to imagine a successful customer-facing business today that does not care a great deal about what people think about their products or services and does not attempt to mine such signals automatically. Many companies have their own platforms where customers can provide reviews directly to them, while other companies specialize in collecting reviews about anything. Of course, all of that happens because people, like you, often want to know the opinions of others before deciding to invest their time or money into something. With the abundance of review data, usually mixing scores and free text, and with the obvious potential economic incentives in quickly figuring out how to improve and better market products, the field of opinion mining has grown very large, very quickly, encompassing both academia and industry.

With over 700 references, the book certainly impresses on the reader the vastness and fast-paced nature of the problem. With the high number of new papers coming out every year, one wonders how many more will there be in a future edition. The book starts with a fairly thorough definition of the main problems related to sentiment analysis, grounded in the psychology of how humans express their thoughts and emotions through text. That discussion is one of the book’s best contributions, despite the fact that the author makes it very clear that none of those theories is universally accepted. The book then changes tack and proceeds on more technical subproblems such as aspect-oriented opinion mining, summarizing multiple reviews, detecting fake reviews, and estimating the quality of the reviews, to name just a few. These chapters are written more like a survey rather than a textbook, in the sense that they list quite a lot of work without synthesizing them into a pedagogical form from which a novice or practitioner could learn the subject. For example, most chapters discuss one or two methods or ideas in a lot of detail, while describing numerous more at a very high level without providing much contrast.

Another positive aspect of the book is that it is full of advice and insight from the author’s vast knowledge and experience in the field, for example, the industrial application of the methods and ideas discussed. Also, while product reviews are the main subject for opinion mining and sentiment analysis, the book does discuss work and ideas applicable to other kinds of text amenable to opinion mining. Finally, the book provides insightful suggestions for future work, pointing out underexplored areas in the domain. Overall, the book seems to be an excellent source for researchers looking for an extensive overview of the area.

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Reviewer:  Denilson Barbosa Review #: CR147373
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