Automatic User-Video Metrics Creations From Emotion Detection

In this digital era, digital content especially video, is increasing in number from time to time. Typically, a video service provider like Youtube will perform video analysis based on the video content such as colours, textures, shapes, and other features that exist in video content. The result of this analysis was used to understand user preference and to personalize video for each user. With technological developments, especially in Machine Learning and Computer Vision technology, video analysis can be based on other things beyond the video. In this context, it is the audience's impression. Thus, with the analysis of audience impressions in real-time, it is expected that the video can be analysed using the emotion parameters of the audience while the video is playing, and this can be done automatically and real-time. This system generates impression statistic for each video which concluded from every user who has watched the video and save those data in the database. Method used to analyse the result is by recruiting respondent and give some questionnaires. Respondents were asked to watch some videos and were asked to compare the impression metric which created by the system with user's real impression. The result shos that the automatic video-metric creation from emotion detection has been able to measure user's impression of the video with more than 80% accuracy stated by 75% of 20 respondents of the survey.