Keywords:ECG, VR, machine learning
By evaluating the effect of the video content, the content creator can know how the viewer felt his work. In this evaluation process, traditionally self-report type questionnaire data has been used. However, since this method involves participant bias, experimenter bias, or human diversity, accurate evaluation is difficult. Also, in order to eliminate these as much as possible, it is necessary to obtain an appropriate subject, which is high cost. In order to deal with these problems, this study proposes a method to complement physiological information in addition to questionnaires when evaluating emotional response to video contents. Specifically, it is a combination of subjective self-report questionnaire and heart rate variability. It is the viewer of short television commercials and news programs that are watched via the VR headset platform. Analysis was done using support vector machine and random forest. As a result, effective models and analysis results were obtained.