Sentiment analysis and opinion mining is a field 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 widely studied in data mining, web mining, and text mining. The importance of sentiment analysis has grown due to the rise of social media such as reviews, forums, blogs, micro-blogs, Twitter, and social networks, which have created a large volume of opinionated data for analysis. Sentiment analysis systems are applied in almost every business and social domain because opinions are central to human activities and influence behaviors. Beliefs and perceptions of reality, and the choices made, are largely conditioned on how others see and evaluate the world. Therefore, when making decisions, people often seek out others' opinions, both individually and organizationally. This book provides a comprehensive introduction and survey of sentiment analysis and opinion mining. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, researchers, and practitioners interested in social media analysis, particularly sentiment analysis. Lecturers can use it in courses on natural language processing, social media analysis, text mining, and data mining. Lecture slides are also available online. The book defines the problem of sentiment analysis and discusses various sub-problems, including opinion spam detection, document sentiment classification, sentence subjectivity and sentiment classification, aspect-based sentiment analysis, sentiment lexicon generation, opinion summarization, comparative opinion analysis, opinion search and retrieval, and quality of reviews. It also includes a preface that highlights the importance of sentiment analysis in human activities and its applications in business and society. The book is structured to provide a structured approach to understanding the unstructured data of natural language, facilitating both qualitative and quantitative analysis of opinions.Sentiment analysis and opinion mining is a field 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 widely studied in data mining, web mining, and text mining. The importance of sentiment analysis has grown due to the rise of social media such as reviews, forums, blogs, micro-blogs, Twitter, and social networks, which have created a large volume of opinionated data for analysis. Sentiment analysis systems are applied in almost every business and social domain because opinions are central to human activities and influence behaviors. Beliefs and perceptions of reality, and the choices made, are largely conditioned on how others see and evaluate the world. Therefore, when making decisions, people often seek out others' opinions, both individually and organizationally. This book provides a comprehensive introduction and survey of sentiment analysis and opinion mining. It covers all important topics and the latest developments in the field with over 400 references. It is suitable for students, researchers, and practitioners interested in social media analysis, particularly sentiment analysis. Lecturers can use it in courses on natural language processing, social media analysis, text mining, and data mining. Lecture slides are also available online. The book defines the problem of sentiment analysis and discusses various sub-problems, including opinion spam detection, document sentiment classification, sentence subjectivity and sentiment classification, aspect-based sentiment analysis, sentiment lexicon generation, opinion summarization, comparative opinion analysis, opinion search and retrieval, and quality of reviews. It also includes a preface that highlights the importance of sentiment analysis in human activities and its applications in business and society. The book is structured to provide a structured approach to understanding the unstructured data of natural language, facilitating both qualitative and quantitative analysis of opinions.