Sentiment Analysis: Mining Opinions, Sentiments, and Emotions by Bing Liu (4-Jun-2015) Hardcover

Studies in Natural Language Processing

by Bing Liu

Sentiment analysis is the computational study of people's opinions, sentiments, emotions, and attitudes. This fascinating problem is increasingly important in business and society. It offers numerous research challenges but promises insight useful to anyone interested in opinion analysis and social media analysis. This book gives a comprehensive introduction to the topic from a primarily natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs that are commonly used to express opinions and sentiments. It covers all core areas of sentiment analysis, includes many emerging themes, such as debate analysis, intention mining, and fake-opinion detection, and presents computational methods to analyze and summarize opinions. It will be a valuable resource for researchers and practitioners in natural language processing, computer science, management sciences, and the social sciences.

Genres: Technology

Unknown Binding

First published March 31, 2015

Reviews

[Ricardo Acuña · Rating 5 out of 5]

Sentiment analysis is a field that is growing rapidly mostly because of the huge data available in the social networks, that make possible many applications to provide information to business, government and media, about the people´s opinions, sentiments and emotions. It is about to extract opinions and sentiments from natural language text using computational methods.

I decided to read this book, mostly to know about the state of the art in the field and its relation to machine learning and natural language processing (NLP) technologies. Liu who is a recognized computer scientist in data mining, machine learning, and NLP wrote this book as an introductory text to sentiment analysis and as a research survey. This book is great in a sense that it gives a comprehensive introduction to the topic, presenting numerous state-of-the-art algorithms in machine learning and NLP. I would say it is not easy to read as it assumes that you know the basics of machine leaning and natural language processing. However if you skip the mathematical parts, you can follow the idea (as I did). It is a great reference book as it cites more than six hundred publications from all major conferences and journals. Liu makes a great job describing some of the most effective machine learning algorithms, like support vector machines (SVMs), naïve Bayes, and conditional random fields (CRFs).

The sentiment analysis in this book have focused primarily on written text and language expressions used to express such feelings. This book it is not really focused in psychology or cognitive analysis of sentiment and emotion. However Liu discuss these ideas briefly in section 2.3.1 Affect, Emotion, and Mood in Psychology, presenting a list of basic emotions from different theorists. Liu says: “To design new emotion detection algorithms, … we should be aware that there is a cognitive gap between people’s true psychological states of mind and the language that they use to express such states.”. The sentiment analysis thus consists in assigning a numerical value to a sentiment, opinion or emotion expressed in a written text. Somehow is an indirect measure of psychological state.

It is a great introductory and reference book in the field of sentiment analysis and opinion mining.