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http://dx.doi.org/10.7236/JIIBC.2017.17.3.167

An Experimental Evaluation of Box office Revenue Prediction through Social Bigdata Analysis and Machine Learning  

Chang, Jae-Young (Dept. of Computer Engineering, Hansung University)
Publication Information
The Journal of the Institute of Internet, Broadcasting and Communication / v.17, no.3, 2017 , pp. 167-173 More about this Journal
Abstract
With increased interest in the fourth industrial revolution represented by artificial intelligence, it has been very active to utilize bigdata and machine learning techniques in almost areas of society. Also, such activities have been realized by development of forecasting systems in various applications. Especially in the movie industry, there have been numerous attempts to predict whether they would be success or not. In the past, most of studies considered only the static factors in the process of prediction, but recently, several efforts are tried to utilize realtime social bigdata produced in SNS. In this paper, we propose the prediction technique utilizing various feedback information such as news articles, blogs and reviews as well as static factors of movies. Additionally, we also experimentally evaluate whether the proposed technique could precisely forecast their revenue targeting on the relatively successful movies.
Keywords
Box office Revenue; Social Bigdata; Machine Learning; Prediction; Reviews;
Citations & Related Records
Times Cited By KSCI : 4  (Citation Analysis)
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1 S. Albert, "Movie Stars and the Distribution of Financially Successful Films in the Motion Picture Industry," Journal of Cultural Economics, Vol.22, No.4, pp.249-270, 1998.   DOI
2 Y. Liu, "Word of Mouth for Movies: Its Dynamics and Impact on Box Office Revenue," Journal of Marketing, Vol.70, No.3, pp.74-89, 2006.   DOI
3 G. Mishne and N. S. Glance, "Predicting Movie Sales from Blogger Sentiment," In AAAI Spring Symposium: Computational Approaches to Analyzing Weblogs, pp.155-158, 2006.
4 L. Lica and M. Tuta, "Predicting Product Performance with Social Media," Informatica Economica , Vol.15, No.2, pp.46-56, 2011.
5 S. Asur and B. A. Huberman, "Predicting the future with social media," Web Intelligence and Intelligent Agent Technology (WI-IAT), 2010 IEEE/WIC/ACM International Conference on. IEEE, 2010. p. 492-499.
6 J. Yim and B. Hwang, Predicting Movie Success based on Machine Learning Using Twitter, KIPS transactions on Software and Data Engineering, Vol. 3, No. 7, pp.263-270. 2014   DOI
7 Y. Kim and J. Hong, A study for the Development of Motion Picture Box-Office Prediction Model, J. of The Korean Statistical Society, Vol. 18, No. 6, pp. 859-869, 2011.
8 O. Lee et al. Movie Box office Analysis using Social Big Data, J. of The Korea Contents Association, Vol. 14, No. 10, 2014
9 S. Cho et al. Predicting Movie Sales through Online Review Mining, Proceedings of the Korea Society of Management Information Systems Conference, 2014
10 S. Jeon and Y. Son, Effect of Online Word-of-Mouth variables as Predictors of Box Office, The Korea Journal of Applied Statistics, Vol. 29, No. 4, pp. 657-678, 2016   DOI
11 S. Lee, J. Cho, C. Kang, and S. Choi, Study on Prediction for a Film Success Using Data Mining, J. of the Korean Data and Information Science Society, Vol. 26, No. 6, pp.1259-1269, 2015.   DOI