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

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Author(s)

    • Li Ji LI Ji
    • Graduate School of Advanced Technology and Science, The University of Tokushima

Abstract

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 F<sub>1</sub> and Macro-averaged F<sub>1</sub> 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.

Journal

  • IEEJ Transactions on Electronics, Information and Systems

    IEEJ Transactions on Electronics, Information and Systems 132(8), 1362-1375, 2012-08-01

    The Institute of Electrical Engineers of Japan

References:  41

Codes

  • NII Article ID (NAID)
    10030937370
  • NII NACSIS-CAT ID (NCID)
    AN10065950
  • Text Lang
    ENG
  • Article Type
    ART
  • ISSN
    03854221
  • NDL Article ID
    023922122
  • NDL Call No.
    Z16-795
  • Data Source
    CJP  NDL  J-STAGE 
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