資料來源:
三民書局
Sentiment analysis : mining opinions, sentiments, and emotions
- 作者： Liu, Bing, 1963- author.
- 其他題名：
- Studies in natural language processing.
- 出版： Cambridge, United Kingdom ;New York, NY : Cambridge University Press
- 版本：Second edition.
- 叢書名： Studies in natural language processing
- 主題： Natural language processing (Computer science) , Computational linguistics. , Public opinion--Data processing. , Data mining. , Discourse analysis--Data processing. , Language and emotions.
- ISBN： 9781108486378 (hbk.): GBP59.99 、 1108486371 (hbk.)
- 資料類型: 圖書
- 內容註: Includes bibliographical references and index.
- 摘要註: "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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讀者標籤：
- 系統號： 005475511 | 機讀編目格式
館藏資訊
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. In addition to traditional computational methods, this second edition includes recent deep learning methods to analyze and summarize sentiments and opinions, and als