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Movie Success Prediction using Historical and Current Data Mining
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 178 - Number 47 |
| Year of Publication: 2019 |
| Authors: Partha Chakraborty, Md. Zahidur Rahman, Saifur Rahman |
10.5120/ijca2019919415
|
Partha Chakraborty, Md. Zahidur Rahman, Saifur Rahman . Movie Success Prediction using Historical and Current Data Mining. International Journal of Computer Applications. 178, 47 ( Sep 2019), 1-5. DOI=10.5120/ijca2019919415
Abstract
Movie industry is a multi-billion-dollar business. Lots of movies are being released in every year. All of these movies have different budgets and different cast crew but one thing in common - all want to make profit from movies i.e. make a good box office record. Success of a movie depends on various factors of past and present. Identifying the right factors can predict the profitability of a movie. Some of the factors in predicting movie success are budget, actors, director, producer, IMDb rating, IMDb metascore, IMDb vote count, rotten tomator’s tomatometer, actors and director social fan following, wikipedia views, trailer views etc.. The success prediction of a movies plays an indispensable job in film industry since it includes immense investments. Be that as it may, success can not be predicted based on a specific property of a movie. To predict success one have to consider all the properties which can affect movie’s success and see how these properties affecting movie’s success over time. In this paper, researchers proposed a model where they consider several factors, each factor is assigned by a weight and success/failure of the upcoming movies is predicted based on the factor’s value.
References
- Nahid Quader, Md. Osman Gani, Dipankar Chaki, and Md. Haider Ali, “A Machine Learning Approach to Predict Movie Box-Office Success,”in 20th International Conference of Computer and Information Technology (ICCIT), December 2017.
- Forbes (2016) ”Experts Predict a Drop in Box Office Revenue In 2016 After a Record Year for Hollywood”, https://www.forbes.com/sites/simonthompson/2016/01/05/ experts-predict-a-drop-in-box-office-revenue-in-2016-after-arecord- year-for-hollywood/402059897195
- Subramaniyaswamy V., Viginesh Vaibhav M., Vishnu Prasad R. and Logesh R., “Predicting Movie Box Office Success using Multiple Regression and SVM,” in the International Conference on Intelligent Sustainable Systems (ICISS 2017).
- J. Valenti (1978). Motion Pictures and Their Impact on Society in the Year 2000, speech given at the Midwest Research Institute, Kansas City, April 25, p. 7.
- M. Saraee, S. White, and J. Eccleston. A data mining approach to analysis and prediction of movie ratings, 2004.
- Ramesh Sharda and Dursun Delen. Predicting box- office success of motion pictures with neural networks. Expert Systems with Applications, vol 30, pp 243-254, 2006.
- W. Zhang and S. Skiena. Improving movie gross pre-diction through news analysis. In Web Intelligence, pages 301-304, 2009.
- Sitaram Asur and Bernardo A. Huberman, Predicting the Future with Social Media, http://arxiv.org/abs/1003.5699, March 2010.
- Michael T. Lash and Kang Zhao, Early Predictions of Movie Success: the Who, What, and When of Profitabil-ity, June 2015.
- Jiawei Han, Jian Pei, and Micheline Kamber. Data Mining Concepts and Techniques, 2012.
- Linear Regression,http://www.stat.yale.edu/Courses/1997-98/ 101/linreg.htm
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