Explainable Neural Network analysis on Movie Success Prediction

S Bhavesh Kumar; Sagar Dhanraj Pande

doi:10.4108/eetsis.4435

Authors

S Bhavesh Kumar Vellore Institute of Technology University
Sagar Dhanraj Pande Vellore Institute of Technology University

DOI:

https://doi.org/10.4108/eetsis.4435

Keywords:

SHAP, ANN, LIME, Glass box model, Black box model, Explanations

Abstract

These days movies are one of the most important part of entertainment industry and back in the days you could see everyday people standing outside theatres, or watching movies in OTT platforms. But due to busy schedules not many people are watching every movie. They go over the internet and search for top rated movies and go to theatres. And creating a successful movie is no easy job. Thus, this study helps movie producers to consider what are the important factors that influence a movie to be successful. this study applied neural network model to the IMDb dataset and then due to its complex nature in order to achieve the local explainability and global explainability for the enhanced analysis, study have used SHAP (Shapley additive explanations) to analysis.

References

W. Zhang and S. Skiena. Improving movie gross pre-diction through news analysis. In Web Intelligence, pages 301-304, 2009.

Michael T. Lash and Kang Zhao, Early Predictions of Movie Success: the Who, What, and When of Profitabil-ity, June 2015.

You Jin Kim, Yun Gyung Cheong, and Jung Hoon Lee. 2019. Prediction of a Movie’s Success From Plot Summaries Using Deep Learning Models. In Proceedings of the Second Workshop on Storytelling, pages 127–135, Florence, Italy. Association for Computational Linguistics.

Sahu, S., Kumar, R., Long, H.V. et al. Early-production stage prediction of movies success using K-fold hybrid deep ensemble learning model. Multimed Tools Appl 82, 4031–4061 (2023).

Ni, Yuan, et al. "Movie Box Office Prediction Based on Multi-Model Ensembles." Information 13.6 (2022): 299.

Wei Lu, Xiaoqiao Zhang, Xinchen Zhan, "Movie Box Office Prediction Based on IFOA-GRNN", Discrete Dynamics in Nature and Society, vol. 2022, Article ID 3690077, 10 pages, 2022.

G. Velingkar, R. Varadarajan, S. Lanka and A. K. M, "Movie Box-Office Success Prediction Using Machine Learning," 2022 Second International Conference on Power, Control and Computing Technologies (ICPC2T), Raipur, India, 2022, pp. 1-6, doi: 10.1109/ICPC2T53885.2022.9776798.

Pande, S., & Khamparia, A. (2023). EXPLAINABLE DEEP NEURAL NETWORK BASED ANALYSIS ON INTRUSION DETECTION SYSTEMS. Computer Science, 24(1).

Guo, M.; Yuan, Z.; Janson, B.; Peng, Y.; Yang, Y.; Wang, W. Older Pedestrian Traffic Crashes Severity Analysis Based on an Emerging Machine Learning XGBoost. Sustainability 2021, 13, 926.

Mokhtari, Karim El, Ben Peachey Higdon, and Ayşe Başar. "Interpreting financial time series with SHAP values." Proceedings of the 29th annual international conference on computer science and software engineering. 2019.

arXiv:1903.02407v2 [cs.LG]

Främling, K. (2020). Decision Theory Meets Explainable AI. Explainable, Transparent Autonomous Agents and Multi-Agent Systems, 12175, 57 - 74. https://doi.org/10.1007/978-3-030-51924-7_4.

Fernandez A., Herrera F., Cordon O., del Jesus M.J., Marcelloni F.: Evolutionary fuzzy systems for explainable artificial intelligence: Why, when, what for, and where to?, IEEE Computational Intelligence Magazine, vol. 14(1), pp. 69–81, 2019.

Holzinger A.: From machine learning to explainable AI. In: 2018 world symposium on digital intelligence for systems and machines (DISA), pp. 55–66, IEEE, 2018.

Ali, Sajid, et al. "Explainable Artificial Intelligence (XAI): What we know and what is left to attain Trustworthy Artificial Intelligence." Information Fusion (2023): 101805.

Borys, Katarzyna, et al. "Explainable AI in medical imaging: An overview for clinical practitioners–Beyond saliency-based XAI approaches." European journal of radiology (2023): 110786.

Welcome to the SHAP documentation — SHAP latest documentation

Linardatos, Pantelis, Vasilis Papastefanopoulos, and Sotiris Kotsiantis. "Explainable ai: A review of machine learning interpretability methods." Entropy 23.1 (2020): 18.

Emmert‐Streib, Frank, Olli Yli‐Harja, and Matthias Dehmer. "Explainable artificial intelligence and machine learning: A reality rooted perspective." Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery 10.6 (2020): e1368.

Xu, Feiyu, et al. "Explainable AI: A brief survey on history, research areas, approaches and challenges." Natural Language Processing and Chinese Computing: 8th CCF International Conference, NLPCC 2019, Dunhuang, China, October 9–14, 2019, Proceedings, Part II 8. Springer International Publishing, 2019.