Predicting Movie Genres Based on Plot Summaries
This work addresses multi-label genre prediction for movies, which is an incremental improvement in text classification for entertainment applications.
The paper tackled the problem of predicting movie genres from plot summaries using various machine learning methods, and found that a GRU-based probabilistic classification with learned threshold achieved the best results, including a Jaccard Index of 50.0%, F-score of 0.56, and hit rate of 80.5%.
This project explores several Machine Learning methods to predict movie genres based on plot summaries. Naive Bayes, Word2Vec+XGBoost and Recurrent Neural Networks are used for text classification, while K-binary transformation, rank method and probabilistic classification with learned probability threshold are employed for the multi-label problem involved in the genre tagging task.Experiments with more than 250,000 movies show that employing the Gated Recurrent Units (GRU) neural networks for the probabilistic classification with learned probability threshold approach achieves the best result on the test set. The model attains a Jaccard Index of 50.0%, a F-score of 0.56, and a hit rate of 80.5%.