A Commonsense Reasoning Framework for Explanatory Emotion Attribution, Generation and Re-classification

• URL: http://arxiv.org/abs/2101.04017v1
• Date: Mon, 11 Jan 2021 16:44:38 GMT
• Title: A Commonsense Reasoning Framework for Explanatory Emotion Attribution, Generation and Re-classification
• Authors: Antonio Lieto, Gian Luca Pozzato, Stefano Zoia, Viviana Patti, Rossana Damiano
• Abstract summary: We present an explainable system for emotion attribution and recommendation (called DEGARI) The system exploits the logic TCL to automatically generate novel commonsense semantic representations of compound emotions. The generated emotions correspond to prototypes, i.e. commonsense representations of given concepts.
• Score: 3.464871689508835
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: In this work we present an explainable system for emotion attribution and recommendation (called DEGARI) relying on a recently introduced commonsense reasoning framework (the TCL logic) which is based on a human-like procedure for the automatic generation of novel concepts in a Description Logics knowledge base. Starting from an ontological formalization of emotions (known as ArsEmotica), the system exploits the logic TCL to automatically generate novel commonsense semantic representations of compound emotions (e.g. Love as derived from the combination of Joy and Trust according to the ArsEmotica model). The generated emotions correspond to prototypes, i.e. commonsense representations of given concepts, and have been used to reclassify emotion-related contents in a variety of artistic domains, ranging from art datasets to the editorial content available in RaiPlay, the online multimedia platform of RAI Radiotelevisione Italiana (the Italian public broadcasting company). We have tested our system (1) by reclassifying the available contents in the tested dataset with respect to the new generated compound emotions (2) with an evaluation, in the form of a controlled user study experiment, of the feasibility of using the obtained reclassifications as recommended emotional content. The obtained results are encouraging and pave the way to many possible further improvements and research directions.

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