The book "Sentiment Analysis and Opinion Mining" by Bing Liu, published as part of the Synthesis Lectures on Human Language Technologies series, provides a comprehensive introduction to the field of sentiment analysis and opinion mining. This field focuses on analyzing people's opinions, sentiments, evaluations, attitudes, and emotions from written language, which is crucial in various business and social domains. The book covers key topics such as problem definitions, different types of opinions, subjectivity and emotion, and various techniques for sentiment classification and opinion summarization. It also discusses cross-domain and cross-language sentiment analysis, aspect-based sentiment analysis, sentiment lexicon generation, and opinion spam detection. The author emphasizes the importance of structured approaches to unstructured natural language text, making it a valuable resource for students, researchers, and practitioners in natural language processing, social media analysis, text mining, and data mining. The book includes over 400 references and is suitable for use in academic courses.The book "Sentiment Analysis and Opinion Mining" by Bing Liu, published as part of the Synthesis Lectures on Human Language Technologies series, provides a comprehensive introduction to the field of sentiment analysis and opinion mining. This field focuses on analyzing people's opinions, sentiments, evaluations, attitudes, and emotions from written language, which is crucial in various business and social domains. The book covers key topics such as problem definitions, different types of opinions, subjectivity and emotion, and various techniques for sentiment classification and opinion summarization. It also discusses cross-domain and cross-language sentiment analysis, aspect-based sentiment analysis, sentiment lexicon generation, and opinion spam detection. The author emphasizes the importance of structured approaches to unstructured natural language text, making it a valuable resource for students, researchers, and practitioners in natural language processing, social media analysis, text mining, and data mining. The book includes over 400 references and is suitable for use in academic courses.