publications
2023
- jadmMoGaL: Novel Movie Graph Construction by Applying LDA on SubtitleM. Nazari, H. Rahmani, D. Momeni, and 1 more authorJournal of AI and Data Mining, 2023
Graph representation of data can better define relationships among data components and thus provide 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. So, 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 extracted the important topics of the movies using LDA on their subtitles. Then, we visualized the relationship between the movies in a graph, using the cosine similarity. Finally, we evaluated the proposed method with respect to measures genre homophily and genre entropy. MoGaL succeeded to outperforms 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.
@article{MoGaL, title = {MoGaL: Novel Movie Graph Construction by Applying LDA on Subtitle}, author = {Nazari, M. and Rahmani, H. and Momeni, D. and Nasiri, M.}, journal = {Journal of AI and Data Mining}, volume = {11}, issue = {2}, pages = {221--228}, year = {2023}, publisher = {Shahrood University of Technology}, url = {https://doi.org/10.22044/jadm.2023.12481.2396}, google_scholar_id = {MXK_kJrjxJIC} }
2022
- icwrConstructing and Analyzing Movie Similarity Graph Based on Topical Analysis of Movie SubtitlesD. Momeni, H. Rahmani, and M. NazariInternational Conference on Web Research, 2022
Nowadays, considering the huge amount of DATA, to search through them, we ought to use methods for analyzing the DATA according to our needs. This challenge also exists in the entertainment and cinema industry to find Movies and TV shows with the same topic aiming to recommend and minimize the search space for the audience. Therefore, methods are needed to efficiently recognize the Movies with the same topic and present them to the users. Most of the existing services lean on user-based information, and usually, not on the original content of the Movies. These services use DATA such as user ratings and comments or features like actors, directors, and the Movie genre or a combination of both. In this paper, we use low-level features of the Movie Subtitles, extracted using LDA, for thematic analysis of textual contents of the Movies (Subtitles). To do so, using the extracted features and Cosine similarity measure, we construct the similarity graph of Movies. In this graph, each node represents a Movie and each edge indicates the similarity between them. In the following, using clustering methods on Movies graphs we were able to achieve a noticeable Thematic correlation between the Movies.
@article{constructingMSGraph, title = {Constructing and Analyzing Movie Similarity Graph Based on Topical Analysis of Movie Subtitles}, author = {Momeni, D. and Rahmani, H. and Nazari, M.}, issue = {8}, year = {2022}, journal = {International Conference on Web Research}, google_scholar_id = {3fE2CSJIrl8C} }
