MoGaL: Novel Movie Graph Construction by Applying LDA on Subtitle
محل انتشار: مجله هوش مصنوعی و داده کاوی، دوره: 11، شماره: 2
سال انتشار: 1402
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 161
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شناسه ملی سند علمی:
JR_JADM-11-2_005
تاریخ نمایه سازی: 27 تیر 1402
چکیده مقاله:
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.
کلیدواژه ها:
نویسندگان
Mohammad Nazari
School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran.
Hossein Rahmani
School of Computer engineering, Iran University of Science and Technology, Tehran, Iran.
Dadfar Momeni
School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
Motahare Nasiri
School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
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