Emotion classification using massive examples extracted from the web

  • Authors:

  • Ryoko Tokuhisa;Kentaro Inui;Yuji Matsumoto

  • Affiliations:

  • Toyota Central R&D Labs., INC., Nagakute Aichi, Japan and Nara Institute of Science and Technology, Ikoma Nara, Japan;Nara Institute of Science and Technology, Ikoma, Nara, Japan;Nara Institute of Science and Technology, Ikoma, Nara, Japan

  • Venue:
  • COLING '08 Proceedings of the 22nd International Conference on Computational Linguistics - Volume 1
  • Year:
  • 2008

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Abstract

In this paper, we propose a data-oriented method for inferring the emotion of a speaker conversing with a dialog system from the semantic content of an utterance. We first fully automatically obtain a huge collection of emotion-provoking event instances from the Web. With Japanese chosen as a target language, about 1.3 million emotion provoking event instances are extracted using an emotion lexicon and lexical patterns. We then decompose the emotion classification task into two sub-steps: sentiment polarity classification (coarsegrained emotion classification), and emotion classification (fine-grained emotion classification). For each subtask, the collection of emotion-proviking event instances is used as labelled examples to train a classifier. The results of our experiments indicate that our method significantly outperforms the baseline method. We also find that compared with the single-step model, which applies the emotion classifier directly to inputs, our two-step model significantly reduces sentiment polarity errors, which are considered fatal errors in real dialog applications.