moviecert-hlstm-unbalanced

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 Unbalanced Hollywood Movie Scripts Age Rating Dataset (1,142 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

• Unbalanced Hollywood Movie Scripts Age Rating Dataset (1,142)
• 1,142 English movie scripts
• Five MPAA age rating categories:
  • G
  • PG
  • PG-13
  • R
  • NC-17

Dataset: https://huggingface.co/datasets/pratikkalamkar/UnBalanced_Hollywood_Movies_Scripts_Age_Rating_Dataset_1142

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