Computer Engineering and Applications ›› 2023, Vol. 59 ›› Issue (3): 33-48.DOI: 10.3778/j.issn.1002-8331.2207-0417

• Research Hotspots and Reviews • Previous Articles     Next Articles

Survey on Emotion Recognition in Conversation

CHEN Xiaoting, LI Shi   

  1. College of Information and Computer Engineering, Northeast Forestry University, Harbin 150040, China
  • Online:2023-02-01 Published:2023-02-01



  1. 东北林业大学 信息与计算机工程学院,哈尔滨 150040

Abstract: Emotion recognition in conversation(ERC) is a hot research topic in the field of emotion computing, which aims to detect the emotion category of each discourse during the dialogue. It has important research significance for dialogue understanding and dialogue generation. At the same time, it has a wide range of practical application value in many fields, such as social media analysis, recommendation system, medical treatment and human-computer interaction. With the continuous innovation and development of deep learning technology, emotion recognition in conversation has attracted more and more attention from academia and industry. At this stage, it is necessary to summarize these research results in an overview article in order to better carry out follow-up work. The research results in this field are comprehensively sorted out from the perspectives of problem definition, problem approach, research methods, and mainstream datasets, and the development of dialogue emotion recognition tasks is reviewed and analyzed. Compared with video and audio, dialogue text contains more information. Therefore, this paper focuses on combing the text dialogue emotion recognition methods, especially the methods based on deep learning. Finally, based on the current research status, this paper summarizes the open problems existing in the field of dialogue emotion recognition and the development trend in the future.

Key words: sentiment analysis, dialogue understanding, emotion recognition in conversation, deep learning

摘要: 对话情绪识别是情感计算领域的一个热门研究课题,旨在检测对话过程中每个话语的情感类别。其在对话理解和对话生成方面具有重要的研究意义,同时在社交媒体分析、推荐系统、医疗和人机交互等诸多领域具有广泛的实际应用价值。随着深度学习技术的不断创新和发展,对话情绪识别受到学术界和工业界越来越多的关注,现阶段需要综述性的文章对已有研究成果进行总结,以便更好地开展后续工作。从问题定义、问题切入方式、研究方法、主流数据集等多个角度对该领域的研究成果进行全面梳理,回顾和分析了对话情绪识别任务的发展。对话文本中含有丰富的语义信息,结合视频和音频可以进一步提升建模效果,因此,重点对文本对话情绪识别以及多模态对话情绪识别的方法进行了梳理,立足于当前研究现状,总结了现有对话情绪识别领域存在的开放问题以及未来的发展趋势。

关键词: 情感分析, 对话理解, 对话情绪识别, 深度学习