Exploitation in Context-Sensitive Affect Sensing from Improvisational Interaction

Abstract

Real-time contextual affect sensing from open-ended multithreaded dialogue is challenging but essential for the building of effective intelligent user interfaces. In this paper, we focus on context-based affect detection using emotion modeling in personal and social communication context. It focuses on the prediction both of the improvisational mood of each character and emotional implication in direct related improvisational context during the creative improvisation. Evaluation results indicate that the new developments on contextual affect sensing enabled an affect inspired AI agent to outperform its previous version in affect sensing tasks.