A Topic Model for Building Fine-grained Domain-specific Emotion Lexicon

Min Yang,Dingju Zhu,K. P. Chow

Published 2014 in Annual Meeting of the Association for Computational Linguistics

ABSTRACT

Emotion lexicons play a crucial role in sentiment analysis and opinion mining. In this paper, we propose a novel Emotion-aware LDA (EaLDA) model to build a domainspecific lexicon for predefined emotions that include anger, disgust, fear, joy, sadness, surprise. The model uses a minimal set of domain-independent seed words as prior knowledge to discover a domainspecific lexicon, learning a fine-grained emotion lexicon much richer and adaptive to a specific domain. By comprehensive experiments, we show that our model can generate a high-quality fine-grained domain-specific emotion lexicon.

PUBLICATION RECORD

  • Publication year

    2014

  • Venue

    Annual Meeting of the Association for Computational Linguistics

  • Publication date

    2014-06-01

  • Fields of study

    Computer Science

  • Identifiers
  • External record

    Open on Semantic Scholar

  • Source metadata

    Semantic Scholar

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