Dimensional Emotion Prediction Based on Interactive Context in Conversation.

Emotion prediction in conversation is important for humans to conduct a fluent conversation, which is an underexplored research topic in the affective computing area. In previous studies, predicting the coming emotion only considered the context information from one single speaker. However, there are two sides of the speaker and listener in interlocutors, and their emotions are influenced by one another during the conversation. For this reason, we propose a dimensional emotion prediction model based on interactive information in conversation from both interlocutors. We investigate the effects of interactive information in four conversation situations on emotion prediction, in which emotional tendencies of interlocutors are consistent or inconsistent in both valence and arousal. The results showed that the proposed method performance better by considering the interactive context information than the ones considering one single side alone. The prediction result is affected by the conversation situations. In the situation interlocutors have consistent emotional tendency in valence and inconsistent tendency in arousal, the prediction performance of valence is the best. In the situation that interlocutors' emotional tendency is inconsistent in both valence and arousal, the prediction performance of arousal is the best.