Emotion Recognition in Conversation: Research Challenges, Datasets, and Recent Advances

By Soujanya Poria, Navonil Majumder, Rada Mihalcea, Eduard H. Hovy. IEEE Access, 2019

455 citations recorded by the source corpus.

Emotion is intrinsic to humans and consequently, emotion understanding is a key part of human-like artificial intelligence (AI). Emotion recognition in conversation (ERC) is becoming increasingly popular as a new research frontier in natural language processing (NLP) due to its ability to mine opinions from the plethora of publicly available conversational data on platforms such as Facebook, Youtube, Reddit, Twitter, and others. Moreover, it has potential applications in health-care systems (as a tool for psychological analysis), education (understanding student frustration), and more. In Addition, ERC is also extremely important for generating emotion-aware dialogues that require an understanding of the user’s emotions. Catering to these needs calls for effective and scalable conversational emotion-recognition algorithms. However, it is a difficult problem to solve because of several research challenges. In this paper, we discuss these challenges and shed light on recent research in this field. We also describe the drawbacks of these approaches and discuss the reasons why they fail to successfully overcome the research challenges in ERC.

Subfields: Artificial Intelligence, Experimental and Cognitive Psychology

View Paper: https://www.semanticscholar.org/paper/4e45f66270407862c8fcd8c1bd5507e09a840b70

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Citations in the corpus

COSMIC: COmmonSense knowledge for eMotion Identification in Conversations (2020, methodology): “MELD (Poria et al., 2019a) is a multimodal dataset extended from the EmotionLines dataset (Chen et al., 2018).”
DialogueGCN: A Graph Convolutional Neural Network for Emotion Recognition in Conversation (2019, methodology): “MELD contains textual, acoustic and visual information for more than 1400 dialogues and 13000 utterances from the Friends TV series.”
Knowledge Aware Emotion Recognition in Textual Conversations via Multi-Task Incremental Transformer (2020, methodology): “Some of datasets, such as MELD, IEMOCAP, EmoryNLP, are multimodal conversation datasets containing textual, acoustic, and visual information.”
Directed Acyclic Graph Network for Conversational Emotion Recognition (2021, background): “Recently, several ERC datasets with textual data have been released (Busso et al., 2008; Schuller et al., 2012; Zahiri and Choi, 2017; Li et al., 2017; Chen et al., 2018; Poria et al., 2019b), arousing the widespread interest of NLP researchers.”
Multimodal sentiment analysis: A systematic review of history, datasets, multimodal fusion methods, applications, challenges and future directions (2022, background): “ERC presents a number of obstacles, including conversational context modelling, interlocutors ’ emotion shift, and others, all of which make the task more difficult to do [79].”
KinGDOM: Knowledge-Guided DOMain Adaptation for Sentiment Analysis (2020, background): “With the advent of graph neural networks, graph-based meth-ods have become a new trend (Ghosal et al., 2019) in diverse NLP tasks such as emotion recognition in conversations (Poria et al., 2019).”
Dealing with Disagreements: Looking Beyond the Majority Vote in Subjective Annotations (2021, background): “Detecting emotions from language has been a signiﬁcant area of research in NLP for the past two decades (Liscombe et al., 2003; Aman and Szpakowicz, 2007; Desmet and Hoste, 2013; Hirschberg and Manning, 2015; Poria et al., 2019).”
Knowledge-Enriched Transformer for Emotion Detection in Textual Conversations (2019, background): “Note that our results for CNN, CNN+cLSTM and DialogueRNN on EC, MELD and IEMOCAP are slightly different from the reported results in (Majumder et al., 2019; Poria et al., 2019).”