A large number of self-made videos on the Internet lack user ratings and the recommendation accuracies of them are not high. In order to solve the problems, a Video Recommendation algorithm based on Danmaku Sentiment Analysis and topic model (VRDSA) was proposed. Firstly, sentiment analysis was performed to video' danmaku comments to obtain the sentiment vectors of the videos, which were used to calculate the emotional similarities between the videos. At the same time, based on the tags of videos, a topic model was built to obtain the topic distribution of the video tags which was used to calculate the topic similarities between the videos. Secondly, the emotional similarities and topic similarities were merged to calculate synthesis similarities between the videos. Thirdly, combined with the comprehensive similarities between the videos and the user's history records, the user preference for videos was obtained. At the same time, the video public recognitions were quantified by user interaction metrics such as the number of likes, danmakus and collections, and the comprehensive recognitions of the videos were calculated by combining the user's history records. Finally, based on the user preference and video comprehensive recognitions, the user's recognitions of videos were predicted, and a personalized recommendation list was generated to complete the video recommendation. Experimental results show that, compared with Danmaku video Recommendation algorithm combing Collaborative Filtering and Topic model (DRCFT) and Unifying LDA (Latent Dirichlet Allocation) and Ratings Collaborative Filtering (ULR-itemCF), the proposed algorithm has the precision increased by 17.1% on average, the recall increased by 22.9% on average, and the F1 increased by 22.2% on average. The proposed algorithm completes the recommendation of videos by analyzing the sentiments of danmakus and integrating the topic model, and fully exploits the emotionality of damaku data to make the recommendation results more accurate.