Research And Application Of Video Recommendation Algorithm Based On Bullet Chat Emotion Analysis And Collaborative Filtering

With the development of society and the continuous innovation of internet technology,the scale of data information has rapidly expanded,and the era of "big data" has quietly arrived.Under background of the "big data" era,every industry is facing the problem of "information overload" caused by a large amount of data,which is more obvious in video media.Therefore,solving the problem of "information overload" in video media is of great significance and value.The recommendation algorithm is a classic algorithm widely used to address the problem of "information overload".However,there are problems such as low accuracy and a lack of reasonable utilization of user emotional information about traditional recommendation algorithms.Especially for current video platforms that integrate bullet chat systems,it is difficult for traditional recommendation algorithms to accurately analyze and push content.To solve these problems,this paper proposes a video recommendation algorithm based on bullet chat sentiment analysis and collaborative filtering.The main research contents are as follows:(1)A sentiment analysis algorithm for bullet chat is proposed,which constructs an sentiment dictionary suitable for bullet chat text.The dictionary accurately calculates the sentiment values of sentiment words inside the bullet chat,achieving effective calculation and classification of bullet chat text sentiment.Compared with the traditional two methods,this algorithm can more accurately analyze the emotional information of bullet chat text and achieve better results.(2)The video recommendation algorithm BEICF based on bullet chat emotion analysis and collaborative filtering is proposed.Starting from the user’s emotional characteristics,a user emotional feature matrix is constructed.With the help of collaborative filtering thought,the KNN algorithm is used to find the recommended content for users,and the video recommendation index is combined to comprehensively obtain the Top-N video recommendation list.After experimental verification,this algorithm improves the accuracy of recommendation and performs well in recommendation performance.(3)The corresponding personalized video recommendation platform is realized by using the video recommendation algorithm based on bullet chat emotion analysis and collaborative filtering proposed in this paper.The system is designed and implemented from the aspects of demand analysis,architecture module design and database design.