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.

Focusing on the issue that most research results on correlation between advertising phrases stay in the literal level, and can not exploit deep semantic information of the phrases, which limits the performance of the task, a novel method was proposed to calculate the correlation between the phrases by using deep learning technique. Recursive AutoEncoder (RAE) was developed to make full use of semantic information in the word order and phrase, which made the phrase vector contain more deep semantic information, and built the calculating method of correlation under the advertising situation. Specifically, for a given list of a few phrases, reconstruction error was produced by merging the adjacent two elements. Phrase tree, which similar to the Huffman tree, was produced by merging two elements with smallest reconstruction error in turn. Gradient descent and Cosine distance were used to minimize the reconstruction error of phrase tree and measure the correlation between the phrases respectively. The experimental results show that the contribution of the important phrases is increased in the representation of the final phrase vector by introducing weight information, and RAE is more suitable for phrase calculation. The proposed method increases the accuracy by 4.59% and 3.21% respectively compared with LDA (Latent Dirichlet Allocation) and BM25 algorithm under the same condition of 50% recall rate, which proves its effectiveness.