Humana Mente 13 (37) (2020)

Authors
Gianmarco Tuccini
Università degli Studi di Firenze
Roberta Lanfredini
Università degli Studi di Firenze
Abstract
Computational models of affect, in their most common form, cannot take into account the qualitative dimension of affect itself. Their expressivity can be extended, thus promoting the much sought-after standardization in the most theory-neutral way, using OWL and machine learning techniques. OWL is an expressive formal language, as well as an established open standard, and can be used to describe the models, possibly including qualitative entities at the fundamental level. The supervised machine learning techniques allow the direct learning and application of models described as OWL ontologies. Thanks to human supervision, they can take into account the qualitative dimension of affect when the models warrant it. To further promote the aforementioned standardization, the task of classifying texts according to their affective content can be recommended as a way to assess the performance of the models. It is a multifaceted task, in which usually divergent fields as philosophy, psychology and computer science meet. Moreover, since it is a very current task in computer science, there are many resources available to facilitate the development of a standard benchmark for CMAS.
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