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Evaluating Real-Time Emotional Responses Using Bullet Screen Sentiment Analysis: Evidence from Electrodermal Activity

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HCI International 2024 – Late Breaking Papers (HCII 2024)

Part of the book series: Lecture Notes in Computer Science ((LNCS,volume 15375))

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Abstract

Bullet screens are attracting increasing attention as a way to express emotions and interact on short video platforms. Prior studies have used natural language processing (NLP) to analyze bullet screen sentiment in order to evaluate public opinion trends regarding a specific topic, movie, or product. However, few studies have investigated the effectiveness of using bullet screen sentiment analysis to predict real-time emotional responses. Thus, this study examined whether and to what extent bullet screen sentiment analysis can be used to evaluate and predict real-time emotional responses to videos by employing physiological electrodermal activity (EDA) measurements. A behavioral experiment was conducted in which eight college students wore a set of wireless galvanic skin sensors while watching three music videos (MVs) in random or-der. The participants’ EDA data, including skin conductance responses and peak amplitudes, were then analyzed. Meanwhile, the sentiments expressed in the bullet screen comments on the three MVs were analyzed using three dictionary-based sentiment analysis algorithms: SnowNLP, BosonNLP, and Hel-loNLP. The bullet screen sentiment analysis and physiological measurement results were then compared using descriptive and correlation analyses. The bullet screen sentiment parameters were found to significantly correlate with the EDA measurements. This study confirms the effectiveness of using bullet screen sentiment analysis to predict participants’ real-time emotional responses, providing a convenient and flexible way for enterprises and governments to detect public opinion trends and take action accordingly.

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Acknowledgments

This study was funded by the National Natural Science Foundation of China [grant number 62207008] and General Program of Stable Support Plan for Universities in Shenzhen [grant number GXWD20231129154726002].

Author information

Authors and Affiliations

  1. School of Humanities and Social Sciences, Harbin Institute of Technology, Shenzhen, China

    Zhao Xu & Qingchuan Li

  2. College of Literature and Journalism, Sichuan University, Chengdu, China

    Yao Song

  3. Digital Convergence Laboratory of Chinese Cultural Inheritance and Global Communication, Sichuan University, Chengdu, China

    Yao Song

Authors
  1. Zhao Xu
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  2. Qingchuan Li
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  3. Yao Song
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Corresponding author

Correspondence to Qingchuan Li .

Editor information

Editors and Affiliations

  1. University of Bucharest, Bucharest, Romania

    Adela Coman

  2. University of Tsukuba, Tsukuba, Japan

    Simona Vasilache

  3. City University of Hong Kong, Kowloon Tong, Hong Kong

    Fiona Fui-Hoon Nah

  4. City University of Hong Kong, Kowloon Tong, Hong Kong

    Keng Leng Siau

  5. University of West Florida, Pensacola, FL, USA

    June Wei

  6. FORTH, Heraklion, Greece

    George Margetis

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Xu, Z., Li, Q., Song, Y. (2025). Evaluating Real-Time Emotional Responses Using Bullet Screen Sentiment Analysis: Evidence from Electrodermal Activity. In: Coman, A., Vasilache, S., Fui-Hoon Nah, F., Siau, K.L., Wei, J., Margetis, G. (eds) HCI International 2024 – Late Breaking Papers. HCII 2024. Lecture Notes in Computer Science, vol 15375. Springer, Cham. https://doi.org/10.1007/978-3-031-76806-4_18

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  • DOI: https://doi.org/10.1007/978-3-031-76806-4_18

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