{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/predicting-movie-genres-based-on-plot","title":"Predicting Movie Genres Based on Plot Summaries","arxiv_id":"1801.04813","date":"2018-01-15","proceeding":null,"authors":["Quan Hoang"],"abstract":"This project explores several Machine Learning methods to predict movie\ngenres based on plot summaries. Naive Bayes, Word2Vec+XGBoost and Recurrent\nNeural Networks are used for text classification, while K-binary\ntransformation, rank method and probabilistic classification with learned\nprobability threshold are employed for the multi-label problem involved in the\ngenre tagging task.Experiments with more than 250,000 movies show that\nemploying the Gated Recurrent Units (GRU) neural networks for the probabilistic\nclassification with learned probability threshold approach achieves the best\nresult on the test set. The model attains a Jaccard Index of 50.0%, a F-score\nof 0.56, and a hit rate of 80.5%.","url_abs":"http://arxiv.org/abs/1801.04813v1","url_pdf":"http://arxiv.org/pdf/1801.04813v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"predicting-movie-genres-based-on-plot","repo_url":"https://github.com/karumugamio/SMAIProject-Team31","is_official":0,"mentioned_in_paper":0,"mentioned_in_github":1,"framework":"tf","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"classification-1","task_name":"Classification"},{"task_slug":"classification","task_name":"General Classification"},{"task_slug":"text-classification","task_name":"Text Classification"},{"task_slug":"text-classification-1","task_name":"text-classification"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}