Kai Wang, Journal of Information Processing Systems Vol. 22, No. 1, pp. 100-116, Feb. 2026  

https://doi.org/10.3745/JIPS.04.0367
Keywords: Barrage Sentiment, Subtitle Topic, Similarity Calculation, Video Recommended
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Abstract

Bullet-screen videos contain rich user-generated data. It is of great practical significance to utilize sentiment analysis technology and topic models to identify video topics by fusing multi-dimensional features. A novel video recommendation method (MSSA) based on multi-source sentiment analysis is proposed by fusing the sentiment features of bullet screens and the topic features of video subtitles. Firstly, the method performs sentiment analysis on the bullet screens and subtitles of videos, and constructs a user sentiment feature matrix and a video sentiment feature matrix. Secondly, the method clusters user groups with similar sentiment tendencies by extracting the temporal information of bullet screens posted by users. Next, the topic feature vectors of subtitle texts in video clips are calculated to obtain the topic similarity matrix among videos by fusing with the video label information. Afterwards, a sentiment-oriented video set is generated according to the differences in sentiment polarity between bullet screens and subtitles. Finally, online recommendation of bullet-screen videos is achieved by introducing a recommendation heat index. The MSSA method is validated on real-world datasets, and it conducts comparative experiments with other state-of-the-art methods to assess its recommendation coverage and accuracy. The experimental results show that the MSSA method can effectively enhance the performance of user sentiment clustering and the semantic alignment of video content topics, enabling it to effectively explore user interest characteristics, and optimize the quality of personalized video recommendation services.

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