Affect Detection from Semantic and Metaphorical Interpretation of Virtual Drama

We have developed an intelligent agent to engage with users in virtual drama improvisation previously. The agent was able to perform sentence-level affect detection especially from inputs with strong emotional indicators. In this research, we employ latent semantic analysis to interpret emotional expressions with vague affect indicators and ambiguous audiences. Latent semantic analysis is thus used to perform topic theme detection and target audience identification for such inputs. Then we also discuss how affect is detected for such inputs without strong emotional indicators with the consideration of emotions expressed by the intended audiences and relationships between speakers and audiences. This work also proves to be effective in recognizing metaphorical phenomena. Moreover, uncertainty-based active learning is also employed to deal with more open-ended and imbalanced affect detection tasks. Overall, this work enables the AI agent to deal with challenging issues in affect detection tasks.