DialogueRNN: An Attentive RNN for Emotion Detection in Conversations

Navonil Majumder, Soujanya Poria, Devamanyu Hazarika, Rada Mihalcea, Alexander Gelbukh +1 more
11/1/2018
cs.CL

Official code: declare-lab/conv-emotion (1,531 stars, MIT, PyTorch, last commit 2024-03-10).

Abstract

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.

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Code Implementations(1)

declare-lab/conv-emotionOfficial100%
PyTorchMIT

This repo contains implementation of different architectures for emotion recognition in conversations.

1,531341Shell, PythonOct 31, 20182 years agoMIT
conversational-agentsconversational-aidialogue-systemsemotion-analysisemotion-recognition+8 more

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Cite this paper

@article{majumder2018dialoguernn,
  title  = {DialogueRNN: An Attentive RNN for Emotion Detection in Conversations},
  author = {Navonil Majumder and Soujanya Poria and Devamanyu Hazarika and Rada Mihalcea and Alexander Gelbukh and Erik Cambria},
  year   = {2018},
  eprint = {1811.00405},
  archivePrefix = {arXiv},
  url    = {https://arxiv.org/abs/1811.00405}
}

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