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With the increasing consumption of video media on various platforms, understanding emotional data and reactions to content is advantageous. This paper introduces TRACE – Transcription and Reaction Analysis through Combined Emotion Detection as a tool designed to detect and analyze emotions contained in video content. By integrating facial recognition and sentiment analysis of transcribed video text, TRACE comprehensively assesses emotional states throughout the video. The project's current version is aimed at utilizing an avatar-based interface to enhance user interactivity by visually representing emotional shifts in real-time. This paper outlines the current development of TRACE, explores some of its technical components, and demonstrates its potential applications in content analysis, sentiment detection, and affective computing research.

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Developing TRACE – Towards an Automated Emotion Detection and Reaction Tool for Media Content

  • 会议论文

  • 发表日期：

    2025年

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  • Michael Roros,
  • James Braman

摘要

With the increasing consumption of video media on various platforms, understanding emotional data and reactions to content is advantageous. This paper introduces TRACE – Transcription and Reaction Analysis through Combined Emotion Detection as a tool designed to detect and analyze emotions contained in video content. By integrating facial recognition and sentiment analysis of transcribed video text, TRACE comprehensively assesses emotional states throughout the video. The project's current version is aimed at utilizing an avatar-based interface to enhance user interactivity by visually representing emotional shifts in real-time. This paper outlines the current development of TRACE, explores some of its technical components, and demonstrates its potential applications in content analysis, sentiment detection, and affective computing research.