license: apache-2.0
datasets:
- pratikkalamkar/UnBalanced_Hollywood_Movies_Scripts_Age_Rating_Dataset_1142
language:
- en
pipeline_tag: text-classification
library_name: keras
metrics:
- accuracy
- precision
- recall
- f1
- mae
tags:
- movie-certification
- movie-scripts
- screenplay
- mpaa-rating
- age-rating
- ordinal-classification
- tf-idf
- mlp
- explainable-ai
- english
- ieee-gcat-2025
TF-IDF + Multi-Layer Perceptron for MPAA Age Rating Prediction
This repository contains a TF-IDF + Multi-Layer Perceptron (MLP) model 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
Task
Predict the appropriate MPAA age rating for a complete English movie script using TF-IDF features and a Multi-Layer Perceptron classifier.
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