Box-office forecasting based on sentiments of movie reviews and Independent subspace method

Box-office forecasting is a challenging but important task for movie distributors in their decision making process. Many previous studies have tried to determine a way to accurately predict the box-office, but the results reported have not been satisfactory for two main reasons: (1) lack of variable diversity and (2) simplicity of forecasting algorithms. Although the importance of word-of-mouth (WOM) has consistently emphasized in past studies, only summarized information, such as volume or valence of user ratings is commonly used. In forecasting algorithms, multiple linear regression is the most popular algorithm because it generates not only predicted values but also variable significances. In this study, new box-office forecasting models are presented to enhance the forecasting accuracy by utilizing review sentiments and employing non-linear machine learning algorithms. Viewer sentiments from review texts are used as input variables in addition to conventional predictors, whereas three machine learning-based algorithms, i.e., classification and regression tree (CART), artificial neural network (ANN), and support vector regression (SVR), are employed to capture non-linear relationship between the box-office and its predictors. In order to provide variable importance for machine learning-based forecasting algorithms, an independent subspace method (ISM) is applied. Forecasting results from six different forecasting periods show that the presented methods can make accurate and robust forecasts. (C) 2016 Elsevier Inc. All rights reserved.This work was supported by (1) the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Science, ICT, & Future Planning (NRF-2014R1A1A1004648), (2) the BK21 Plus Program (Center for Sustainable and Innovative Industrial Systems, Dept. of Industrial Engineering, Seoul National University) funded by the Ministry of Education, Korea (No. 21A20130012638), (3) the National Research Foundation(NRF) grant funded by the Korea government (MSIP) (No. NRF-2015R1A2A2A04007359), and (4) the Institute for Industrial Systems Innovation of SNU.OAIID:RECH_ACHV_DSTSH_NO:T201621914RECH_ACHV_FG:RR00200001ADJUST_YN:EMP_ID:A004522CITE_RATE:3.364DEPT_NM:산업공학과EMAIL:zoon@snu.ac.krSCOPUS_YN:YCONFIRM: