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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.
- Sales Rank: #945844 in Books
- Published on: 2015-06-04
- Original language: English
- Number of items: 1
- Dimensions: 9.21" h x .94" w x 6.14" l, 1.61 pounds
- Binding: Hardcover
- 381 pages
About the Author
Bing Liu is a Professor of Computer Science at the University of Illinois. His current research interests include sentiment analysis and opinion mining, data mining, machine learning, and natural language processing. He has published extensively in top conferences and journals, and his research has been cited on the front page of the New York Times. He is also the author of two books: Sentiment Analysis and Opinion Mining (2012) and Web Data Mining: Exploring Hyperlinks, Contents and Usage Data (1st edition, 2007; 2nd edition, 2011). He currently serves as the chair of ACM SIGKDD and is an IEEE Fellow.
Most helpful customer reviews
1 of 1 people found the following review helpful.
Sentiment Analysis Explained In Plain English Wonderfully
By William P Ross
Liu does a masterful job explaining Sentiment Analysis in a way that highly technical yet understandable. There is a great introduction explaining aspects of theory and systems in production. Throughout the book the author tells you which techniques only work in theory versus those that work in practice. A large amount of other texts and studies are referenced with a full bibliography in the back.
The beginning chapters start off by explaining how to tell if a sentence or document is positive or negative. Later more advanced topics are discussed like entity extraction and comparisons. The chapters are in a logical order which builds nicely on the previous ones.
Technical aspects of this book are concise and to the point. The level of explanations are understandable. That is not to say that this is an easy book; there are formulas especially in chapter 6 which are challenging. Latent Dirichlet Allocation is complicated no matter who is going to explain it, but this book makes it manageable.
The author is familiar with pretty much all the studies done on Sentiment Analysis as you will see reading this book. He openly admits which areas of Sentiment Analysis are underdeveloped and is critical of his own field in certain situations. The goal of the author appears to be to bridge the gap of the social scientist and the computer scientist, and with this book he is taking us one step closer.
1 of 1 people found the following review helpful.
... read some chapters of the book and I really liked it. It is similar to previous book of ...
By vahid
I read some chapters of the book and I really liked it. It is similar to previous book of Liu (sentiment analysis and opinion mining) but much better and more comprehensive than it. I suggest it to anyone who wants to enter this area.
0 of 0 people found the following review helpful.
Bible for sentiment analysis
By Life long learner
This is a really good review. I wish if author has also offered his critique on various approaches and given his opinion on:
1) what approaches are promising over others
2) what approaches are gaining more popularity among the community
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