  Emotion detection in conversations is a necessary step for a number of
applications, including opinion mining over chat history, social media threads,
debates, argumentation mining, understanding consumer feedback in live
conversations, etc. Currently, systems do not treat the parties in the
conversation individually by adapting to the speaker of each utterance. In this
paper, we describe a new method based on recurrent neural networks that keeps
track of the individual party states throughout the conversation and uses this
information for emotion classification. Our model outperforms the state of the
art by a significant margin on two different datasets.
