The implementation of movies and TV plays analysis system combined with knowledge graph and data visualization

Abstract

More and more movies and television plays have been produced in recent years, but a few have succeeded in the market. Therefore, the analysis and speculation of the success factors of movies and television plays are very important for the producers and investors. The existing analysis platform only analyzes the benefits generated by movies and television dramas in a certain period and lacks the ability of prediction and reasoning. To analyze the key factors affecting the success of movies and television dramas and provide a reference for producers and investors, we design and implement a movies and television plays analysis system combined with a knowledge graph and data visualization technology. First of all, we crawl the information of movies and television plays and user comments on the Douban website; Then, the entities and relationships are extracted by OpenUE toolkit, and Neo4j is used to construct and store the knowledge graph in movies and television plays. On this basis, we utilize the improved TransR algorithm for knowledge completion and reasoning. Finally, combined with the knowledge graph, we analyze the success factors of popular movies and TV plays and visualize the analysis results in various chart types.