Arsemotica: what is this
Aims, desired output, approach
Context: Affective computing + Social Semantic Web framework
Aim: extracting a rich emotional semantics (i.e. not limited to a positive or a negative reception) of tagged resources. Sentiment Analysis and beyond!
Desired output: not merely a general appreciation but a set of specific concepts, that emerge as the most significant in capturing the emotions of the users towards the resource at issue, each enriched with a score expressing the strength of that emotion.
Approach: ontology-driven sentiment analysis. Given a tagged resource, the correlation between tags and emotions is computed by exploiting and combining Semantic Web tools and lexical resources.The extraction of the emotional content is driven by an ontology of emotional categories: from unstructured texts to concepts of an ontology. Intuitively:
- tags directly referring to ontological concepts are identified;
- potentially affective tags can be identified by users by using the ontology;
- the final emotional output for the given resource is calculated based on identified emotional concepts by exploiting automated reasoning on ontology relationships.
The whole process is organized in 4 main phases which are sketched in the architecture commented below.