Emotions and Textual Feature Analysis for Motion Picture Association of America (MPAA) Rating Classification

Yaseen Abbas and Ahmed Al-Azawei

References:

1. M. Shafaei, N. S. Samghabadi, S. Kar, and T. Solorio, “Age suitability rating: Predicting the MPAA rating based on movie dialogues,” in LREC 2020 - 12th International Conference on Language Resources and Evaluation, Conference Proceedings, 2020.
2. L. Deni Setiawan and B. Bestari Puspita, “classification of children short films for mobile movie screening by bioscil,” 2019. doi: 10.17501/24246778.2019.5101.
3. L. A. Ha and E. Mohamed, “Combining Text and Images for Film Age Appropriateness Classification,” in Procedia CIRP, 2021. doi: 10.1016/j.procs.2021.05.087.
4. R. Jayashree and A. Nayan Varma, “MPAA Rating Prediction Using Script Analysis for Movies,” in 2022 IEEE 7th International conference for Convergence in Technology, I2CT 2022, 2022. doi: 10.1109/I2CT54291.2022.9825434.
5. V. R. Martinez, K. Somandepalli, K. Singla, A. Ramakrishna, Y. T. Uhls, and S. Narayanan, “Violence rating prediction from movie scripts,” in 33rd AAAI Conference on Artificial Intelligence, AAAI 2019, 31st Innovative Applications of Artificial Intelligence Conference, IAAI 2019 and the 9th AAAI Symposium on Educational Advances in Artificial Intelligence, EAAI 2019, 2019. doi: 10.1609/aaai.v33i01.3301671.
6. V. Sanh, L. Debut, J. Chaumond, and T. Wolf, “DistilBERT, a distilled version of BERT: smaller, faster, cheaper and lighter,” arXiv preprint arXiv:1910.01108, 2019.
7. G. Ke, Q. Meng, T. Finley, T. Wang, W. Chen, W. Ma, Q. Ye, and T.-Y. Liu, “LightGBM: A highly efficient gradient boosting decision tree,” in Adv. Neural Inf. Process. Syst., vol. 30, 2017.
8. M. Shafaei, N. S. Samghabadi, S. Kar, and T. Solorio, “Rating for Parents: Predicting Children Suitability Rating for Movies Based on Language of the Movies,” arXiv preprint arXiv:1908.07819, Aug. 2019, [Online]. Available: http://arxiv.org/abs/1908.07819
9. M. Shafaei, C. Smailis, I. A. Kakadiaris, and T. Solorio, “A Case Study of Deep Learning Based Multi-Modal Methods for Predicting the Age-Suitability Rating of Movie Trailers,” arXiv preprint arXiv:2101.11704, Jan. 2021, [Online]. Available: http://arxiv.org/abs/2101.11704
10. Y. Liu, M. Ott, N. Goyal, J. Du, M. Joshi, D. Chen, O. Levy, M. Lewis, L. Zettlemoyer, and V. Stoyanov, “RoBERTa: A robustly optimized BERT pretraining approach,” arXiv preprint arXiv:1907.11692, 2019.
11. D. Demszky, D. Movshovitz-Attias, J. Ko, A. Cowen, G. Nemade, and S. Ravi, “GoEmotions: A dataset of fine-grained emotions,” in Proceedings of the Annual Meeting of the Association for Computational Linguistics, 2020. doi: 10.18653/v1/2020.acl-main.372.
12. K. S. Jones, “A statistical interpretation of term specificity and its application in retrieval,” 1972. doi: 10.1108/eb026526.
13. S. W. Kim and J. M. Gil, “Research paper classification systems based on TF-IDF and LDA schemes,” Human-centric Computing and Information Sciences, vol. 9, no. 1, 2019, doi: 10.1186/s13673-019-0192-7.
14. M. Siino, I. Tinnirello, and M. La Cascia, “Is text preprocessing still worth the time? A comparative survey on the influence of popular preprocessing methods on Transformers and traditional classifiers,” Inf Syst, vol. 121, 2024, doi: 10.1016/j.is.2023.102342.
15. M. Sokolova and G. Lapalme, “A systematic analysis of performance measures for classification tasks,” Inf Process Manag, vol. 45, no. 4, pp. 427–437, Jul. 2009, doi: 10.1016/j.ipm.2009.03.002.
16. C. Goutte and E. Gaussier, “A Probabilistic Interpretation of Precision, Recall and F-Score, with Implication for Evaluation,” in Lecture Notes in Computer Science, 2005. doi: 10.1007/978-3-540-31865-1_25.