Table of Contents Author Guidelines Submit a Manuscript
Computational Intelligence and Neuroscience
Volume 2017 (2017), Article ID 4315419, 16 pages
Research Article

Box Office Forecasting considering Competitive Environment and Word-of-Mouth in Social Networks: A Case Study of Korean Film Market

1Department of Industrial and Management Engineering, Hanbat National University, Daejeon, Republic of Korea
2Department of Industrial and Systems Engineering, Seoul National University of Science and Technology, Seoul, Republic of Korea
3School of Industrial Management Engineering, Korea University, Seoul, Republic of Korea

Correspondence should be addressed to Pilsung Kang;

Received 20 February 2017; Accepted 18 June 2017; Published 27 July 2017

Academic Editor: Sandhya Samarasinghe

Copyright © 2017 Taegu Kim et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Accurate box office forecasting models are developed by considering competition and word-of-mouth (WOM) effects in addition to screening-related information. Nationality, genre, ratings, and distributors of motion pictures running concurrently with the target motion picture are used to describe the competition, whereas the numbers of informative, positive, and negative mentions posted on social network services (SNS) are used to gauge the atmosphere spread by WOM. Among these candidate variables, only significant variables are selected by genetic algorithm (GA), based on which machine learning algorithms are trained to build forecasting models. The forecasts are combined to improve forecasting performance. Experimental results on the Korean film market show that the forecasting accuracy in early screening periods can be significantly improved by considering competition. In addition, WOM has a stronger influence on total box office forecasting. Considering both competition and WOM improves forecasting performance to a larger extent than when only one of them is considered.