Affect detection from open-ended text-based dialogue and contextual profiles is challenging but essential for the building of effective intelligent user interfaces. In this paper, we report updated developments of an affect detection model from text, including affect detection from two particular types of metaphorical affective expressions (food and cooking metaphors) and affect detection based on context. We use Markov chains and dynamic algorithm to simulate the improvisational mood of a particular character for contextual affect analysis and explore an alternative approach for emotional context modelling in a comparatively complex story context. The overall affect detection model has been embedded in an intelligent conversational AI agent interacting with human users under loose scenarios. Evaluation for the updated affect detection component is also provided. Our work contributes to the journal themes on human-agent interaction, affective computing and dialogue based systems.
|Journal||International Journal of Computational Linguistics Research|
|Publication status||Published - Sep 2010|