Toward a Computational Approach for Natural Language Description of Emotions

  • Abe Kazemzadeh
Part of the Lecture Notes in Computer Science book series (LNCS, volume 6975)


This is a précis of the author’s dissertation proposal about natural language description of emotions. The proposal seeks to explain how humans describe emotions using natural language. The focus of the proposal is on words and phrases that refer to emotions, rather than the more general phenomena of emotional language. The main problem is that if descriptions of emotions refer to abstract concepts that are local to a particular human (or agent), then how do these concepts vary from person to person and how can shared meaning be established between people. The thesis of the proposal is that natural language emotion descriptions refer to theoretical objects, which provide a logical framework for dealing with this phenomenon in scientific experiments and engineering solutions. An experiment, Emotion Twenty Questions (EMO20Q), was devised to study the social natural language behavior of humans, who must use descriptions of emotions to play the familiar game of twenty questions when the unknown word is an emotion. The idea of a theory based on natural language propositions is developed and used to formalize the knowledge of a sign-using organism. Based on this pilot data, it was seen that approximately 25% of the emotion descriptions referred to emotions as objects with dimensional attributes. This motivated the author to use interval type-2 fuzzy sets as a computational model for the meaning of this dimensional subset of emotion descriptions. This model introduces a definition of a variable that ranges over emotions and allows for both inter- and intra- subject variability. A second set of experiments used interval surveys and translation tasks to assess this model. Finally, the use of spectral graph theory is proposed to represent emotional knowledge that has been acquired from the EMO20Q game.


Natural Language Emotion Word Emotional Information Formal Concept Analysis Theoretical Object 
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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© Springer-Verlag Berlin Heidelberg 2011

Authors and Affiliations

  • Abe Kazemzadeh
    • 1
  1. 1.University of Southern CaliforniaUSA

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