ID | 118261 |
著者 | |
キーワード | word emotion classification
complex emotion
emotion intensity prediction
emotion-topic variation
hierarchical Bayesian network
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資料タイプ |
学術雑誌論文
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抄録 | In this paper, we provide a Word Emotion Topic (WET) model to predict the complex word emotion information from text, and discover the distribution of emotions among different topics. A complex emotion is defined as the combination of one or more singular emotions from following 8 basic emotion categories: joy, love, expectation, surprise, anxiety, sorrow, anger and hate. We use a hierarchical Bayesian network to model the emotions and topics in the text. Both the complex emotions and topics are drawn from raw texts, without considering any complicated language features. Our experiment shows promising results of word emotion prediction, which outperforms the traditional parsing methods such as the Hidden Markov Model and the Conditional Random Fields(CRFs) on raw text. We also explore the topic distribution by examining the emotion topic variation in an emotion topic diagram.
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掲載誌名 |
China Communications
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ISSN | 16735447
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出版者 | China Communications Magazine
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巻 | 9
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号 | 3
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開始ページ | 99
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終了ページ | 109
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発行日 | 2012-03
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EDB ID | |
フルテキストファイル | |
言語 |
eng
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著者版フラグ |
出版社版
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部局 |
理工学系
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