Key points are not available for this paper at this time.
This study aims to uncover the intrinsic links between emotional experiences and physiological responses as users watch short videos on social media platforms, with a particular focus on using changes in physiological signals to identify and understand different emotional states. To achieve this objective, a simulated experiment was designed to browse short videos and induce and record physiological signals under seven typical emotional states. The recorded signals included electroencephalogram, galvanic skin response, skin temperature, and heart rate, resulting in the creation of a dataset. Machine learning algorithms were employed to classify emotions and evaluate the dataset's effectiveness. Statistical testing methods were used to analyze signal feature changes and distributions across different emotional states, exploring trends and their statistical significance. The study successfully constructed an emotion-physiological signal dataset. Statistical tests revealed significant changes in physiological signal characteristics across different emotional states, providing extensive data support for understanding how emotions specifically affect physiological responses. The research not only confirmed the practicality of the constructed dataset in emotion recognition tasks but also provided empirical evidence of how emotions influence physiological responses through detailed analysis of physiological signals. The findings of this study hold significant value for emotional science, psychological research, and the entertainment industry. They not only facilitate a deeper exploration of individual emotional physiological mechanisms but also provide a scientific basis for optimizing content recommendation systems, intelligent entertainment technologies, and affective-aware applications.
Liao et al. (Mon,) studied this question.