DialogueRNN: An Attentive RNN for Emotion Detection in Conversations

Understand

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.