Detecting emotions in social affective situations using the emotinet knowledge base

Authors:
Alexandra Balahur;Jesús M. Hermida;Andrés Montoyo
Affiliations:
Department of Software and Computing Systems, University of Alicante, Alicante, Spain;Department of Software and Computing Systems, University of Alicante, Alicante, Spain;Department of Software and Computing Systems, University of Alicante, Alicante, Spain
Venue:
ISNN'11 Proceedings of the 8th international conference on Advances in neural networks - Volume Part III
Year:
2011

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Abstract

The task of automatically detecting emotion in text is challenging. This is due to the fact that most of the times, textual expressions of affect are not direct - using emotion words - but result from the interpretation and assessment of the meaning of the concepts and their interaction, described in the chains of actions presented. This article presents the core of EmotiNet, a knowledge base (KB) for representing and storing affective reaction to real-life contexts and action chains described in text, and the methodology employed in designing, populating, extending and evaluating it. The basis of the design process is given by a set of self-reported affective situations in the International Survey on Emotion Antecedents and Reactions corpus. From the evaluation performed, we conclude that our final model represents a semantic resource appropriate for capturing and storing the semantics of real actions and predict the emotional responses triggered by chains of actions.