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<p>Emotion recognition plays a vital role in human–computer interaction and mental health assessment. Despite rapid evolution from psychological approaches to AI-driven methods, the field lacks a systematic understanding of its knowledge structure and research patterns. This scientometric analysis examined 39,686 articles from the Web of Science (2004–2023), revealing the field's intellectual structure and major research clusters. Our findings show the United States, China, and the United Kingdom lead the field, yet research collaboration remains fragmented. The analysis identifies three major transitions: from single-modal to multimodal analysis, from laboratory settings to real-world applications, and from algorithm development to addressing practical challenges (exemplified by innovations in mask detection during COVID-19). This study is limited by its reliance on a single database source and exclusion of non-English publications, which may introduce regional and linguistic biases in the findings. Future research should incorporate multiple databases and multilingual sources to provide more comprehensive insights. This work provides a framework for understanding emotion recognition research development, offering insights for both theoretical advancement and practical applications.</p>

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The global landscape of emotion recognition research from 2004 to 2023: a scientometric and visualization analysis

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年5月18日

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  • Peng Liu,
  • Changzheng Yang,
  • Kai Wang

摘要

Emotion recognition plays a vital role in human–computer interaction and mental health assessment. Despite rapid evolution from psychological approaches to AI-driven methods, the field lacks a systematic understanding of its knowledge structure and research patterns. This scientometric analysis examined 39,686 articles from the Web of Science (2004–2023), revealing the field's intellectual structure and major research clusters. Our findings show the United States, China, and the United Kingdom lead the field, yet research collaboration remains fragmented. The analysis identifies three major transitions: from single-modal to multimodal analysis, from laboratory settings to real-world applications, and from algorithm development to addressing practical challenges (exemplified by innovations in mask detection during COVID-19). This study is limited by its reliance on a single database source and exclusion of non-English publications, which may introduce regional and linguistic biases in the findings. Future research should incorporate multiple databases and multilingual sources to provide more comprehensive insights. This work provides a framework for understanding emotion recognition research development, offering insights for both theoretical advancement and practical applications.