Annotation and analysis of emotionally relevant behavior in the ISL meeting corpus

We present an annotation scheme for emotionally relevant behavior at the speaker contribution level in multiparty conversation. The scheme was applied to a large, publicly available meeting corpus by three annotators, and subsequently labeled with emotional valence. We report inter-labeler agreement statistics for the two schemes, and explore the correlation between speaker valence and behavior, as well as that between speaker valence and the previous speaker's behavior. Our analyses show that the co-occurrence of certain behaviors and valence classes significantly deviates from what is to be expected by chance; in isolated cases, behaviors are predictive of valence.