DialogueRNN: An Attentive RNN for Emotion Detection in Conversations


  • Navonil Majumder Instituto Politécnico Nacional
  • Soujanya Poria Nanyang Technological University
  • Devamanyu Hazarika National University of Singapore
  • Rada Mihalcea University of Michigan
  • Alexander Gelbukh Instituto Politécnico Nacional
  • Erik Cambria Nanyang Technological University




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, and so on. 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.




How to Cite

Majumder, N., Poria, S., Hazarika, D., Mihalcea, R., Gelbukh, A., & Cambria, E. (2019). DialogueRNN: An Attentive RNN for Emotion Detection in Conversations. Proceedings of the AAAI Conference on Artificial Intelligence, 33(01), 6818-6825. https://doi.org/10.1609/aaai.v33i01.33016818



AAAI Technical Track: Natural Language Processing