pratikkalamkar/moviecert-hlstm-balanced

Hierarchical LSTM with Ordinal Learning for MPAA Age Rating Prediction

This repository contains a Hierarchical Long Short-Term Memory (HLSTM) model with ordinal learning trained on the Balanced Hollywood Movie Scripts Age Rating Dataset (250 scripts) for automated MPAA age rating prediction from full-length English movie scripts.

The model was developed as part of the following publication:

Hierarchical Ordinal Framework for Automated Movie Censorship Using Full-Length Scripts

Dataset

• Balanced Hollywood Movie Scripts Age Rating Dataset (250)
• 250 English movie scripts
• Balanced across five MPAA age rating categories:
  • G
  • PG
  • PG-13
  • R
  • NC-17
• 50 scripts per class

Dataset: https://huggingface.co/datasets/pratikkalamkar/Balanced_Hollywood_Movies_Scripts_Age_Rating_Dataset_250

Task

Predict the appropriate MPAA age rating for a complete English movie script using a Hierarchical LSTM architecture with ordinal learning.

Citation

If you use this model in your research, please cite:

@inproceedings{kalamkar2025hierarchical,
  author    = {Pratik N. Kalamkar and Yogesh K. Sharma},
  title     = {Hierarchical Ordinal Framework for Automated Movie Censorship Using Full-Length Scripts},
  booktitle = {2025 IEEE 6th Global Conference for Advancement in Technology (GCAT)},
  year      = {2025},
  pages     = {1--7},
  doi        = {10.1109/GCAT66372.2025.11368510}
}

or

P. N. Kalamkar and Y. K. Sharma, "Hierarchical Ordinal Framework for Automated Movie Censorship Using Full-Length Scripts," 2025 IEEE 6th Global Conference for Advancement in Technology (GCAT), Bangalore, India, 2025, pp. 1-7, doi: 10.1109/GCAT66372.2025.11368510.

License

Apache License 2.0