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