Beyond Sentiments and Opinions: Exploring Social Media with Appraisal Categories

The digital era arrives with a whole set of disruptive technologies that creates both risk and opportunity for open sources analysis. Although the sheer quantity of online conversations makes social media a huge source of information, their analysis is still a challenging task and many of traditional methods and research methodologies for data mining are not fit for purpose. Social data mining revolves around subjective content analysis, which deals with the computational processing of texts conveying people's evaluations, beliefs, attitudes and emotions. Opinion mining and sentiment analysis are the main paradigm of social media exploration and both concepts are often interchangeable. This paper investigates the use of appraisal categories to explore data gleaned for social media, going beyond the limitations of traditional sentiment and opinion-oriented approaches. Categories of appraisal are grounded on cognitive foundations of the appraisal theory, according to which people's emotional response are based on their own evaluative judgments or appraisals of situations, events or objects. A formal model is developed to describe and explain the way language is used in the cyberspace to evaluate, express mood and subjective states, construct personal standpoints and manage interpersonal interactions and relationships. A general processing framework is implemented to illustrate how the model is used to analyze a collection of tweets related to extremist attitudes.