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EmotiNet: A Knowledge Base for Emotion Detection in Text Built on the Appraisal Theories

  • Alexandra Balahur
  • Jesús M. Hermida
  • Andrés Montoyo
  • Rafael Muñoz
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6716)

Abstract

The automatic detection of emotions is a difficult task in Artificial Intelligence. In the field of Natural Language Processing, the challenge of automatically detecting emotion from text has been tackled from many perspectives. Nonetheless, the majority of the approaches contemplated only the word level. Due to the fact that emotion is most of the times not expressed through specific words, but by evoking situations that have a commonsense affective meaning, the performance of existing systems is low. This article presents the EmotiNet knowledge base – a resource for the detection of emotion from text based on commonsense knowledge on concepts, their interaction and their affective consequence. The core of the resource is built from a set of self-reported affective situations and extended with external sources of commonsense knowledge on emotion-triggering concepts. The results of the preliminary evaluations show that the approach is appropriate for capturing and storing the structure and the semantics of real situations and predict the emotional responses triggered by actions presented in text.

Keywords

EmotiNet emotion detection emotion ontology knowledge base appraisal theories self-reported affect action chain 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Alexandra Balahur
    • 1
  • Jesús M. Hermida
    • 1
  • Andrés Montoyo
    • 1
  • Rafael Muñoz
    • 1
  1. 1.Department of Software and Computing SystemsUniversity of AlicanteAlicanteSpain

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