Design of a Movie Review Rating Prediction (MR2P) Algorithm

Authors

  • Oluwatofunmi Adetunji  Software Engineering Department, Babcock University, Nigeria
  • Mamudu Hadiza  Computer Science Department, Babcock University, Nigeria
  • Nzechukwu Otuneme  Computer Science Department, Wesley University, Nigeria

DOI:

https://doi.org//10.32628/CSEIT206461

Keywords:

Movie Rating, Prediction Algorithm, Movie Success Prediction, Entertainment

Abstract

Entertainment is no longer just anything that we enjoy occasionally, with over two million spectators a day, the amount generated by the movie industry is huge. The movie sector is one of the biggest contributors to the entertainment industry’s unpredictability in success and failure. The aim of this research work to design an efficient movie recommendation algorithm that will increase prediction accuracy, the Movie Review Rating Prediction (MR2P) was achieved through a systematic review of the existing movie success algorithm. This research work will enable movie stakeholders (producers, directors, crew, cast already in the movie industry or aspirants) to know the kind of movie to invest in which will, in turn, be beneficial in terms of higher profit.

References

  1. Antara, U., Nivedita, K., Shalin, S., Tanisha, M., & Pranali, W. (2018). Movie Success Prediction Using Data Mining. International Journal of Engineering Development and Research, 6(4), 198–203.
  2. Bhave, A., Kulkarni, H., Biramane, V., & Kosamkar, P. (2015). Role of different factors in predicting movie success. 2015 International Conference on Pervasive Computing: Advance Communication Technology and Application for Society, ICPC 2015. https://doi.org/10.1109/PERVASIVE.2015.7087152
  3. Dhir, R., & Raj, A. (2018). Movie Success Prediction using Machine Learning Algorithms and their Comparison. ICSCCC 2018 - 1st International Conference on Secure Cyber Computing and Communications, 385–390. https://doi.org/10.1109/ICSCCC.2018.8703320
  4. Gaikar, D., Solanki, R., Shinde, H., Phapale, P., & Pandey, I. (2019). Movie Success Prediction Using Popularity Factor from Social Media. International Research Journal of Engineering and Technology, 6(4), 5185–5190. Retrieved from www.irjet.net
  5. Hennig-Thurau, T., Houston, M. B., Hennig-Thurau, T., & Houston, M. B. (2019). The Fundamentals of Entertainment. In Entertainment Science (pp. 41–57). https://doi.org/10.1007/978-3-319-89292-4_2
  6. Kumar, S., Mehta, A., & Joy, P. (2019). Movie Success Prediction using Data Mining. Vellore Institute of Technology.
  7. Lash, M. T., & Zhao, K. (2016). Early Predictions of Movie Success: The Who, What, and When of Profitability. Journal of Management Information Systems, 33(3), 874–903. https://doi.org/10.1080/07421222.2016.1243969
  8. Meenakshi, K., Maragatham, G., Agarwal, N., & Ghosh, I. (2018). A Data mining Technique for Analyzing and Predicting the success of Movie. Journal of Physics: Conference Series, 1000(1). https://doi.org/10.1088/1742-6596/1000/1/012100
  9. Ng, S. (2012). A Brief History of Entertainment Technologies. Proceedings of the IEEE, 100(SPL CONTENT), 1386–1390. https://doi.org/10.1109/JPROC.2012.2189805
  10. Nielsen, B. (2006). Order Determination in General Vector Autoregressions. In Time Series and Related Topics (pp. 93–112). https://doi.org/10.1214/074921706000000978
  11. Patel, M. (2018). Data Structure and Algorithm With C (1st ed.). Educreation Publishing, New Delhi.
  12. Quail, C., Razzano, K., & Skalli, L. (2007). Vulture Culture: The Politics and Pedagogy of Daytime Television Talk Shows. Peter Lang Publishing Inc., New York.
  13. Raj, M. P. M., & Aditya, S. (2017). Predictive Model for Movie’s Success and Sentiment Analysis. Research Journal of Management Sciences, 6(6), 1–7.
  14. Ranjan, S., & Sood, S. (2018). Analyzing Social Media Community Sentiment Score for Prediction of Success of Bollywood Movies. International Journal of Latest Engineering and Management Research, 3(2), 80–88.
  15. Shah, K., Kapadia, J., Samel, Y., Saple, S., & Deshmane, P. (2019). Movie Success Prediction using Data Mining and Social Media. International Research Journal of Engineering and Technology, 6(3), 188–190.
  16. Sharma, P., & Kaur, M. (2013). Classification in Pattern Recognition: A Review. International Journal of Advanced Research in Computer Science and Software Engineering, 3(4), 2277–128. Retrieved from www.ijarcsse.com
  17. Subramaniyaswamy, V., Logesh, R., Chandrashekhar, M., Challa, A., & Vijayakumar, V. (2017). A Personalised Movie Recommendation System Based on Collaborative Filtering. International Journal of High Performance Computing and Networking, 10(1–2), 54–63. https://doi.org/10.1504/IJHPCN.2017.083199

IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology

Downloads

Published

2020-08-30

Issue

Volume 6, Issue 4, July-August-2020

Section

Research Articles

License

Copyright (c) IJSRCSEIT

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

[1]
Oluwatofunmi Adetunji, Mamudu Hadiza, Nzechukwu Otuneme, " Design of a Movie Review Rating Prediction (MR2P) Algorithm, IInternational Journal of Scientific Research in Computer Science, Engineering and Information Technology(IJSRCSEIT), ISSN : 2456-3307, Volume 6, Issue 4, pp.423-432, July-August-2020. Available at doi : https://doi.org/10.32628/CSEIT206461