2023/12/07 by Qinglan Wei, Wei, Qinglan, Yaqi Zhou +3
Computer Science · Psychology · #Emotion and Mood Recognition #FOS: Computer and information sciences #Sentiment Analysis and Opinion Mining #Social and Information Networks (cs.SI) #Video Analysis and Summarization
paper · pdf · doi:10.48550/arxiv.2312.04279
openalex publication_date 2023/12/07 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
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.