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ID 118261
著者
康, 鑫 The University of Tokushima 徳島大学 教育研究者総覧
任, 福継 The University of Tokushima|Hefei University of Technology 徳島大学 教育研究者総覧 KAKEN研究者をさがす
キーワード
word emotion classification
complex emotion
emotion intensity prediction
emotion-topic variation
hierarchical Bayesian network
資料タイプ
学術雑誌論文
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.
掲載誌名
China Communications
ISSN
16735447
出版者
China Communications Magazine
9
3
開始ページ
99
終了ページ
109
発行日
2012-03
EDB ID
フルテキストファイル
言語
eng
著者版フラグ
出版社版
部局
理工学系