S-Space College of Engineering/Engineering Practice School (공과대학/대학원) Dept. of Industrial Engineering (산업공학과) Journal Papers (저널논문_산업공학과)
Box-office forecasting based on sentiments of movie reviews and Independent subspace method
- Hur, Minhoe; Kang, Pilsung; Cho, Sungzoon
- Issue Date
- ELSEVIER SCIENCE INC
- Information Science, Vol.372, pp. 608-624
- Box-office forecasting based on sentiments of movie reviews and Independent subspace method; 복합학; Box-office forecasting; Motion pictures; Sentiment analysis; Movie reviews; Independent subspace model
- 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.
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