From Tags to Emotions: Ontology-driven Sentiment Analysis in the Social Semantic Web

Affective computing is receiving increasing attention in many sectors, ranging from advertisement to politics. This work, set in a Social Semantic Web framework, presents ArsEmotica, an application software for associating the predominant emotions to artistic resources of a social tagging platform. Our aim is to extract a rich emotional semantics (i.e. not limited to a positive or a negative reception) of tagged resources through an ontology driven approach. This is done by exploiting and combining available computational and sentiment lexicons with an ontology of emotional categories. The information sources we rely upon are the tags by which users annotated resources, that are available through the ArsMeteo platform, and the ontology OntoEmotion, that was enriched by means of our tool with over four hundred Italian emotional words referring to the about eighty-five emotional concepts of the ontology. Tags directly referring to ontological concepts are identified, while potentially affective tags can be annotated by using the ontology, thanks to the spontaneous intervention of the users, in a pure Web 2.0 approach. Finally, the tagged artworks are related with the emerging predominant emotions. A user study involving the ArsMeteo community was conducted in order to evaluate the ArsEmotica outcome, for what concerns the emotions automatically associated by the system to the artworks.