Predicting and evoking listener's emotion in online dialogue

Abstract

While there have been many attempts to estimate the emotion of a speaker from her/his utterance, few studies have explored how her/his utterance affects the emotion of the listener. This has motivated us to investigate two novel tasks: predicting the emotion of the listener and generating a response that evokes a specific emotion in the listener's mind. We target Japanese Twitter posts as a source of dialogue data and automatically build training data for learning the predictors and generators. The feasibility of our approaches is assessed by using 1099 utterance-response pairs that are built by five human workers.