A Multidimensional Culturally Adapted Representation of Emotions for Affective Computational Simulation and Recognition

One of the main challenges in affective computing is the development of models to represent the information that is inherent to emotions. It is necessary to consider that the terms used by humans to name emotions depend on the culture and language used. This article presents an experiment-based method to represent and adapt emotion terms to different cultural environments. We propose using circular boxplots to analyze the distribution of emotions in the Pleasure-Arousal space. From the results of this analysis, we define a new cross-cultural representation model of emotions in which each emotion term is assigned to an area in the Pleasure-Arousal space. An emotion is represented by a vector in which the direction indicates the type, and the module indicates the intensity of the emotion. We propose two methods based on fuzzy logic to represent and express emotions: the emotion representation process in which the term associated with the recognized emotion is defuzzified and projected as a vector in the Pleasure-Arousal space; and the emotion expression process in which a fuzzification of the vector is produced, generating a fuzzy emotion term that is adapted to the culture and language in which the emotion will be used.