Paper: Predicting and Eliciting Addressee’s Emotion in Online Dialogue

ACL ID P13-1095
Title Predicting and Eliciting Addressee’s Emotion in Online Dialogue
Venue Annual Meeting of the Association of Computational Linguistics
Session Main Conference
Year 2013
Authors

While there have been many attempts to estimate the emotion of an addresser from her/his utterance, few studies have ex- plored how her/his utterance affects the emotion of the addressee. This has mo- tivated us to investigate two novel tasks: predicting the emotion of the addressee and generating a response that elicits a specific emotion in the addressee?s mind. We target Japanese Twitter posts as a source of dialogue data and automatically build training data for learning the pre- dictors and generators. The feasibility of our approaches is assessed by using 1099 utterance-response pairs that are built by five human workers.