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Sentiment analysis : mining opinions, sentiments, and emotions

By: Liu, Bing.
Series: Studies in natural language processing.Publisher: Cambridge : Cambridge University Press, 2020Edition: 2nd ed.Description: xvii, 431 p. ; ill., 24 cm.ISBN: 9781108486378.Subject(s): Computational linguistics | Data mining | Public opinion | Data processing | Natural language processing | Discourse analysis | Computer science | Language and emotions | Aspect extraction | Context - dependent sentiment | Emotion lexicon | Entity resolution | Fact - implied opinion | Intensifier | Lexicon - based approach | Opinion Parser | Sarcasm | Fake review detection | Spam detectionDDC classification: 006.312 Summary: Sentiment analysis is the computational study of people's opinions, sentiments, emotions, moods, and attitudes. This fascinating problem offers numerous research challenges, but promises insight useful to anyone interested in opinion analysis and social media analysis. This comprehensive introduction to the topic takes a natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs commonly used to express opinions, sentiments, and emotions. The book covers core areas of sentiment analysis and also includes related topics such as debate analysis, intention mining, and fake-opinion detection. It will be a valuable resource for researchers and practitioners in natural language processing, computer science, management sciences, and the social sciences
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Item type Current location Call number Status Date due Barcode
Books 006.312 LIU (Browse shelf) Checked out 15/05/2024 032555

Includes bibliographical references and index.

Sentiment analysis is the computational study of people's opinions, sentiments, emotions, moods, and attitudes. This fascinating problem offers numerous research challenges, but promises insight useful to anyone interested in opinion analysis and social media analysis. This comprehensive introduction to the topic takes a natural-language-processing point of view to help readers understand the underlying structure of the problem and the language constructs commonly used to express opinions, sentiments, and emotions. The book covers core areas of sentiment analysis and also includes related topics such as debate analysis, intention mining, and fake-opinion detection. It will be a valuable resource for researchers and practitioners in natural language processing, computer science, management sciences, and the social sciences

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