2018/01/15 by Quan Hoang, Hoang, Quan · 11 citations
Computer Science · Decision Sciences · Mathematics · #Artificial intelligence #Artificial neural network #Binary classification #Computation and Language (cs.CL) #Computer science #FOS: Computer and information sciences #Jaccard index #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Machine learning #Mathematics #Music and Audio Processing #Naive Bayes classifier #Pattern recognition (psychology) #Plot (graphics) #Probabilistic classification #Probabilistic logic #Rank (graph theory) #Set (abstract data type) #Statistics #Stock Market Forecasting Methods #Support vector machine #Task (project management) #Test set #Transformation (genetics) #Video Analysis and Summarization #Word2vec #cs.CL #cs.LG #stat.ML
paper · pdf · doi:10.48550/arxiv.1801.04813
published in arXiv (Cornell University) (Cornell University)
arxiv created 2018/01/15 · openalex publication_date 2018/01/15 · arxiv updated 2018/01/16 · openalex created_date 2025/10/10 · openalex updated_date 2026/08/04
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%.