# Predicting Movie Genres Based on Plot Summaries

arXiv: https://arxiv.org/abs/1801.04813 · HTML: https://mlsanity.com/paper/1801.04813/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 15 Jan 2018.

## Built on by (most cited first, 4 of 4)

- [DYPLODOC: Dynamic Plots for Document Classification](https://mlsanity.com/paper/2107.12226/) (2021)
- [Demystifying Visual Features of Movie Posters for Multi-Label Genre Identification](https://mlsanity.com/paper/2309.12022/) (2023)
- [BERT-Flow-VAE: A Weakly-supervised Model for Multi-Label Text Classification](https://mlsanity.com/paper/2210.15225/) (2022)
- [Unraveling Movie Genres through Cross-Attention Fusion of Bi-Modal Synergy of Poster](https://mlsanity.com/paper/2410.19764/) (2024)

## Abstract (arXiv)

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%.