Emotion Recognition of Weblog Sentences Based on an Ensemble Algorithm of Multi-label Classification and Word Emotions

カテゴリ: 論文誌(論文単位)

グループ名: 【C】電子・情報・システム部門

発行日: 2012/08/01

タイトル(英語): Emotion Recognition of Weblog Sentences Based on an Ensemble Algorithm of Multi-label Classification and Word Emotions

著者名: Ji Li (Graduate School of Advanced Technology and Science, The University of Tokushima), Fuji Ren (Institute of Technology and Science, The University of Tokushima)

著者名(英語): Ji Li (Graduate School of Advanced Technology and Science, The University of Tokushima), Fuji Ren (Institute of Technology and Science, The University of Tokushima)

キーワード: Emotion Recognition，Weblog，Natural Language Processing，Multi-label Classification，Machine Learning

要約(英語): Weblogs have greatly changed the communication ways of mankind. Affective analysis of blog posts is found valuable for many applications such as text-to-speech synthesis or computer-assisted recommendation. Traditional emotion recognition in text based on single-label classification can not satisfy higher requirements of affective computing. In this paper, the automatic identification of sentence emotion in weblogs is modeled as a multi-label text categorization task. Experiments are carried out on 12273 blog sentences from the Chinese emotion corpus Ren_CECps with 8-dimension emotion annotation. An ensemble algorithm RAKEL is used to recognize dominant emotions from the writer's perspective. Our emotion feature using detailed intensity representation for word emotions outperforms the other main features such as the word frequency feature and the traditional lexicon-based feature. In order to deal with relatively complex sentences, we integrate grammatical characteristics of punctuations, disjunctive connectives, modification relations and negation into features. It achieves 13.51% and 12.49% increases for Micro-averaged F1 and Macro-averaged F1 respectively compared to the traditional lexicon-based feature. Result shows that multiple-dimension emotion representation with grammatical features can efficiently classify sentence emotion in a multi-label problem.

本誌: 電気学会論文誌C（電子・情報・システム部門誌） Vol.132 No.8 （2012） 特集：光・量子ビームによるナノダイナミクス

本誌掲載ページ: 1362-1375 p

原稿種別: 論文／英語

電子版へのリンク: https://www.jstage.jst.go.jp/article/ieejeiss/132/8/132_1362/_article/-char/ja/