SINN: A speaker influence aware neural network model for emotion detection in conversations

Inferring the sentiment polarity or emotion category of subjective text is the fundamental task of sentiment analysis. Recently, emotion detection in conversations that considering context utterances has emerged as a very important and challenging task in this line of research. Most existing studies do not distinguish different speakers in a dialog and fail to characterize inter-speaker dependencies for emotion detection. In this paper, we propose a S peaker I nfluence aware N eural N etwork model (dubbed as SINN) to predict the emotion of the last utterance in a conversation, which explicitly models the self and inter-speaker influences of historical utterances with GRUs (Gated Recurrent Units) and hierarchical attention matching network. Moreover, the empathy phenomenon is also considered by an emotion state tracking component in SINN. Finally, the target utterance representation is enhanced by speaker influence aware context modeling, where an attention mechanism is used to extract the most relevant features for emotion classification. We construct a large-scale multi-turn Chinese dialog dataset WBEmoDialog, where each utterance is manually annotated with an emotion label. Extensive experiments are conducted on public available DailyDialog dataset as well as our constructed WBEmoDialog dataset, and the results show that our model can achieve better or comparable performance with the strong baseline methods.