Abe Kazemzadeh, Sungbok Lee, and Shrikanth S. Narayanan. Fuzzy Logic Models for the Meaning of Emotion Words. IEEE Computational Intelligence Magazine, 8(2):34–49, May 2013.

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Abstract

This paper presents two models that use interval type-2 fuzzy sets (IT2 FSs) for representing themeaning of words that refer to emotions. In the first model, the meaning of an emotion word is representedby IT2 FSs on valence, activation, and dominance scales. In the second model, the meaning of an emotionword is represented by answers to an open-ended set of questions from the game of Emotion TwentyQuestions (EMO20Q). The notion of meaning in the two proposed models is made explicit using theFregean framework of extensional and intensional components of meaning. Inter- and intra-subject uncertainty is captured by using IT2 FSs learned from interval approach surveys. Similarity and subsethood operators are used for comparing the meaning of pairs of words. For the first model, we apply similarity andsubsethood operators for the task of translating one emotional vocabulary, represented as a computing withwords (CWW) codebook, to another. This act of translation is shown to be an example of CWW that isextended to use the three scales of valence, activation, and dominance to represent a single variable. Weexperimentally evaluate the use of the first model for translations and mappings between vocabularies.Accuracy is high when using a small emotion vocabulary as an output, but performance decreases when theoutput vocabulary is larger. The second model was devised to deal with larger emotion vocabularies, butpresents interesting technical challenges in that the set of scales underlying two different emotion wordsmay not be the same. We evaluate the second model by comparing it with results from a single-slider survey.We discuss the theoretical insights that the two models allow and the advantages and disadvantages of each.

BibTeX Entry

@article{Kazemzadeh2013FuzzyLogicModelsfor,
 abstract = {This paper presents two models that use interval type-2 fuzzy sets (IT2 FSs) for representing the
meaning of words that refer to emotions. In the first model, the meaning of an emotion word is represented
by IT2 FSs on valence, activation, and dominance scales. In the second model, the meaning of an emotion
word is represented by answers to an open-ended set of questions from the game of Emotion Twenty
Questions (EMO20Q). The notion of meaning in the two proposed models is made explicit using the
Fregean framework of extensional and intensional components of meaning. Inter- and intra-subject uncertainty is captured by using IT2 FSs learned from interval approach surveys. Similarity and subsethood operators are used for comparing the meaning of pairs of words. For the first model, we apply similarity and
subsethood operators for the task of translating one emotional vocabulary, represented as a computing with
words (CWW) codebook, to another. This act of translation is shown to be an example of CWW that is
extended to use the three scales of valence, activation, and dominance to represent a single variable. We
experimentally evaluate the use of the first model for translations and mappings between vocabularies.
Accuracy is high when using a small emotion vocabulary as an output, but performance decreases when the
output vocabulary is larger. The second model was devised to deal with larger emotion vocabularies, but
presents interesting technical challenges in that the set of scales underlying two different emotion words
may not be the same. We evaluate the second model by comparing it with results from a single-slider survey.
We discuss the theoretical insights that the two models allow and the advantages and disadvantages of each.},
 author = {Kazemzadeh, Abe and Lee, Sungbok and Narayanan, Shrikanth S.},
 bib2html_rescat = {emotion},
 doi = {10.1109/MCI.2013.2247824},
 journal = {IEEE Computational Intelligence Magazine},
 link = {http://sail.usc.edu/publications/files/abeFuzzyLogicModelsForTheMeaningOfEmotionWords.pdf},
 month = {May},
 number = {2},
 pages = {34-49},
 title = {Fuzzy Logic Models for the Meaning of Emotion Words},
 volume = {8},
 year = {2013}
}

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