MoGaL: Novel Movie Graph Construction by Applying LDA on Subtitle
- سال انتشار: 1402
- محل انتشار: مجله هوش مصنوعی و داده کاوی، دوره: 11، شماره: 2
- کد COI اختصاصی: JR_JADM-11-2_005
- زبان مقاله: انگلیسی
- تعداد مشاهده: 228
نویسندگان
School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran.
School of Computer engineering, Iran University of Science and Technology, Tehran, Iran.
School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
School of Computer Engineering, Iran University of Science and Technology, Tehran, Iran
چکیده
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
Subtitle analysis, Movies graph, Graph Analysis، Graph entropy, Graph homophilyاطلاعات بیشتر در مورد COI
COI مخفف عبارت CIVILICA Object Identifier به معنی شناسه سیویلیکا برای اسناد است. COI کدی است که مطابق محل انتشار، به مقالات کنفرانسها و ژورنالهای داخل کشور به هنگام نمایه سازی بر روی پایگاه استنادی سیویلیکا اختصاص می یابد.
کد COI به مفهوم کد ملی اسناد نمایه شده در سیویلیکا است و کدی یکتا و ثابت است و به همین دلیل همواره قابلیت استناد و پیگیری دارد.