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A System for Affect Analysis of Utterances in Japanese Supported with Web Mining

JJSF 21-2 194-213.pdf1.02 MBPDF見る/開く

タイトル: A System for Affect Analysis of Utterances in Japanese Supported with Web Mining
著者: Ptaszynski, Michal 著作を一覧する
Dybala, Pawel 著作を一覧する
Shi, Wenhan 著作を一覧する
Rzepka, Rafal 著作を一覧する
Araki, Kenji 著作を一覧する
キーワード: Affect analysis
Analysis of emotiveness
Web mining
Evaluation methods
発行日: 2009年 6月30日
出版者: Japan Society for Fuzzy Theory and Intelligent Informatics = 日本知能情報ファジィ学会
誌名: Journal of Japan Society for Fuzzy Theory and Intelligent Informatics = 知能と情報
巻: 21
号: 2
開始ページ: 194
終了ページ: 213
出版社 DOI: 10.3156/jsoft.21.194
抄録: We propose a method for affect analysis of textual input in Japanese supported with Web mining. The method is based on a pragmatic reasoning that emotional states of a speaker are conveyed by emotional expressions used in emotive utterances. It means that if an emotive expression is used in a sentence in a context described as emotive, the emotion conveyed in the text is revealed by the used emotive expression. The system ML-Ask (Emotive Elements / Expressions Analysis System) is constructed on the basis of this idea. An evaluation of the system is performed in which two evaluation methods are compared. To choose the most objective evaluation method we compare the most popular method in the field and a method proposed by us. The proposed evaluation method was shown to be more objective and revealed the strong and weak points of the system in detail. In the evaluation experiment ML-Ask reached human level in recognizing the general emotiveness of an utterance (0.83 balanced F-score) and 63% of human level in recognizing the specific types of emotions. We support the system with a Web mining technique to improve the performance of emotional state types extraction. In the Web mining technique emotive associations are extracted from the Web using co-occurrences of emotive expressions with morphemes of causality. The Web mining technique improved the performance of the emotional states types extraction to 85% of human performance.
資料タイプ: article
出現コレクション:雑誌発表論文等 (Peer-reviewed Journal Articles, etc)

提供者: Rafal Rzepka


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