MoGaL: Novel Movie Graph Construction by Applying LDA on Subtitle

Graph representation of data can better define the relationships among the data components, and thus provide a better and richer analysis. So far, movies have been represented in graphs many times using different features for clustering, genre prediction, and even for use in recommender systems. In constructing movie graphs, little attention has been paid to their textual features such as subtitles, while they contain the entire content of the movie, and there is a lot of hidden information in them. Thus in this paper, we propose a method called MoGaL to construct movie graph using LDA on subtitles. In this method, each node is a movie, and each edge represents the novel relationship discovered by MoGaL among two associated movies. First, we extract the important topics of the movies using LDA on their subtitles. Then we visualize the relationship between the movies in a graph using the cosine similarity. Finally, we evaluate the proposed method with respect to the measured genre homophily and genre entropy. MoGaL succeeds to outperform the baseline method significantly in these measures. Accordingly, our empirical results indicate that movie subtitles could be considered a rich source of informative information for various movie analysis tasks.