  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.
