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

TL;DR

This paper introduces DEGARI, an explainable emotion attribution system using TCL logic to generate and reclassify emotions based on a formal ontological model, demonstrating promising results across artistic and media content domains.

Contribution

The paper presents a novel framework combining TCL logic with an ontological emotion model to automatically generate and reclassify complex emotions in various content types.

Findings

Encouraging reclassification results in artistic and media content.

Positive user ratings for emotion reclassifications.

Enhanced explainability of emotion attribution process.

Abstract

We present DEGARI (Dynamic Emotion Generator And ReclassIfier), an explainable system for emotion attribution and recommendation. This system relies 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 based on the Plutchik model, 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 Plutchik). 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 contents available in RaiPlay, the online platform of RAI Radiotelevisione Italiana (the Italian public broadcasting company). We show how the reported results (evaluated in the light of the obtained reclassifications, the user ratings assigned to such reclassifications, and their explainability) are encouraging, and pave the way to many further research directions.