Sentic Medoids: Organizing Affective Common Sense Knowledge in a Multi-Dimensional Vector Space

Abstract

Existing approaches to opinion mining and sentiment analysis mainly rely on parts of text in which opinions and sentiments are explicitly expressed such as polarity terms and affect words. However, opinions and sentiments are often conveyed implicitly through context and domain dependent concepts, which make purely syntactical approaches ineffective. To overcome this problem, we have recently proposed Sentic Computing, a multi-disciplinary approach to opinion mining and sentiment analysis that exploits both computer and social sciences to better recognize and process opinions and sentiments over the Web. Among other tools, Sentic Computing includes AffectiveSpace, a language visualization system that transforms natural language from a linguistic form into a multi-dimensional space. In this work, we present a new technique to better cluster this vector space and, hence, better organize and reason on the affective common sense knowledge in it contained.