Textual Affect Sensing and Affective Communication

Abstract

Unlike sentiment analysis which detects positive, negative, or neutral sentences, textual affect sensing tries to detect more detailed affective or emotional states appearing in text, such as joy, sadness, anger, fear, disgust, surprise and much more. The authors describe here their following two approaches for textual affect sensing: The first one detects nine emotions using a set of rules implemented on the basis of a linguistic compositionality principle for textual affect interpretation. This process includes symbolic cue processing, detection and transformation of abbreviations, sentence parsing, and word/phrase/sentence-level analyses. The second one challenged to recognize 22 emotion types defined in the OCC (Ortony, Clore & Collins) emotion model, which is the most comprehensive emotion model and employs several cognitive variables. In this research, we have shown how these cognitive variables of the emotion model can be computed from linguistic components in text. These two approaches have exploited detailed level analyses of text in two different ways more than ever towards textual affect sensing. Applications towards affective communication are also outlined, including affective instant messaging, affective chat in 3D virtual world, affective haptic interaction, and online news classification relying on affect.