EmotionML

  • Felix Burkhardt
  • Catherine Pelachaud
  • Björn W. Schuller
  • Enrico Zovato
Chapter
First Online:

Abstract

EmotionML is a W3C recommendation to represent emotion related states in data processing systems. Given the lack of agreement in the literature on the most relevant aspects of emotion, it is important to provide a relatively rich set of descriptive mechanisms. It is possible to use EmotionML both as a standalone markup and as a plug-in annotation in different contexts. Emotions can be represented in terms of four types of descriptions taken from the scientific literature: categories, dimensions, appraisals, and action tendencies, with a single <emotion> element containing one or more of such descriptors. EmotionML provides a set of emotion vocabularies taken from the scientific and psychology literature. Whenever users have a need for a different vocabulary, however, they can simply define their own custom vocabulary and use it in the same way as the suggested vocabularies. Several applications have already been realized on the basis of EmotionML.

Keywords

Emotion Recognition Sentiment Analysis Action Tendency Emotion Category Markup Language 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.
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Copyright information

© Springer International Publishing Switzerland 2017

Authors and Affiliations

  • Felix Burkhardt
    • 1
  • Catherine Pelachaud
    • 2
  • Björn W. Schuller
    • 3
    • 4
  • Enrico Zovato
    • 5
  1. 1.Telekom Innovation LaboratoriesBerlinGermany
  2. 2.LTCI, CNRS, Télécom ParisTechUniversité Paris-SaclayParisFrance
  3. 3.Imperial CollegeLondonUK
  4. 4.University of Passau, Chair CISPassauGermany
  5. 5.NuanceTurinItaly

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