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http://dx.doi.org/10.5351/KJAS.2016.29.7.1257

Prediction of box office using data mining  

Jeon, Seonghyeon (Department of Statistics, Chonnam National University)
Son, Young Sook (Department of Statistics, Chonnam National University)
Publication Information
The Korean Journal of Applied Statistics / v.29, no.7, 2016 , pp. 1257-1270 More about this Journal
Abstract
This study deals with the prediction of the total number of movie audiences as a measure for the box office. Prediction is performed by classification techniques of data mining such as decision tree, multilayer perceptron(MLP) neural network model, multinomial logit model, and support vector machine over time such as before movie release, release day, after release one week, and after release two weeks. Predictors used are: online word-of-mouth(OWOM) variables such as the portal movie rating, the number of the portal movie rater, and blog; in addition, other variables include showing the inherent properties of the film (such as nationality, grade, release month, release season, directors, actors, distributors, the number of audiences, and screens). When using 10-fold cross validation technique, the accuracy of the neural network model showed more than 90 % higher predictability before movie release. In addition, it can be seen that the accuracy of the prediction increases by adding estimates of the final OWOM variables as predictors.
Keywords
data mining; decision tree; multilayer perceptron(MLP) neural network; multinomial logit model; online word-of-mouth(OWOM); prediction of box office; support vector machine; 10-fold cross validation;
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Times Cited By KSCI : 4  (Citation Analysis)
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1 Jeon, S. and Son, Y.S. (2016). Effect of online word-of-mouth variables as predictors of box office, The Korean Journal of Applied Statistics, 29, 657-678.   DOI
2 Kim, T., Hong, J., and Koo, H. (2013). Forecasting box-office revenue by considering social network services in the Korean market, Journal Teknologi (Social Sciences), 64, 97-101.
3 Kim, Y.H. and Hong, J.H. (2011). A study for the development of motion picture box-office prediction model, Communications for Statistical Applications and Methods, 18, 859-869.   DOI
4 Korean Film Council (2015). 2015 Korean film consumer survey, Korean Film.
5 Korean Film Council (2016). 2015 Korean film industry settlement, Korean Film, 71.
6 SAS Institute Inc (2012). Getting started with SAS Enterprise Miner 12.1, SAS Institute Inc., Cary.
7 Sharda, R. and Delen, D. (2006). Predicting box-office success of motion pictures with neural networks, Expert Systems with Applications, 30, 243-254.   DOI
8 Song, J. and Han, S. (2013). Predicting gross box office revenue for domestic films, Communications for Statistical Applications and Methods, 20, 301-309.   DOI
9 Yim, J. and Hwang, B. (2014). Predicting movie success based on machine learning using twitter, KIPS Transactions on Software and Data Engineering, 3, 263-270.   DOI
10 Zhang, L., Luo, J., and Yang, S. (2009). Forecasting box office revenue of movies with BP neural network, Expert Systems with Applications, 36, 6580-6587.   DOI