DialogueRNN: An Attentive RNN for Emotion Detection in Conversations
This addresses the problem of accurately detecting emotions in multi-party conversations for applications like opinion mining and consumer feedback analysis, representing a novel method for a known bottleneck.
The paper tackles emotion detection in conversations by introducing a recurrent neural network that tracks individual party states, achieving significant improvements over state-of-the-art methods on two datasets.
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.