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YouTube Shorts, a new section launched by YouTube in 2021, is a directcompetitor to short video platforms like TikTok. It reflects the rising demandfor short video content among online users. Social media platforms are oftenflooded with short videos that capture different perspectives and emotions onhot events. These videos can go viral and have a significant impact on thepublic's mood and views. However, short videos' affective computing was aneglected area of research in the past. Monitoring the public's emotionsthrough these videos requires a lot of time and effort, which may not be enoughto prevent undesirable outcomes. In this paper, we create the first multimodaldataset of short video news covering hot events. We also propose an automatictechnique for audio segmenting and transcribing. In addition, we improve theaccuracy of the multimodal affective computing model by about 4.17% byoptimizing it. Moreover, a novel system MSEVA for emotion analysis of shortvideos is proposed. Achieving good results on the bili-news dataset, the MSEVAsystem applies the multimodal emotion analysis method in the real world. It ishelpful to conduct timely public opinion guidance and stop the spread ofnegative emotions. Data and code from our investigations can be accessed at:http://xxx.github.com.

MSEVA : A System for Multimodal Short Videos Emotion Visual Analysis