Exploration of metaphor and affect sensing using semantic interpretation in an intelligent agent

We developed a virtual drama improvisation platform to allow human users to be creative in their role-play with the interaction of an AI agent. Previously, the AI agent was able to detect affect from users' inputs with strong affect indicators. In this paper, we integrate context-based affect detection to enable the intelligent agent to detect affect from inputs with weak or no affect signals. Topic theme detection using latent semantic analysis is applied to such inputs to identify their discussions themes and potential target audiences. Relationships between characters are also taken into account for affect analysis. Such semantic interpretation of the dialogue contexts also proofs to be effective in the recognition of metaphorical phenomena