Browse

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

Cited 27 time in Web of Science Cited 29 time in Scopus
Authors
Hur, Minhoe; Kang, Pilsung; Cho, Sungzoon
Issue Date
2016-12
Publisher
ELSEVIER SCIENCE INC
Citation
Information Science, Vol.372, pp. 608-624
Keywords
Box-office forecasting based on sentiments of movie reviews and Independent subspace method복합학Box-office forecastingMotion picturesSentiment analysisMovie reviewsIndependent subspace model
Abstract
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.
ISSN
0020-0255
Language
English
URI
http://hdl.handle.net/10371/116909
DOI
https://doi.org/10.1016/j.ins.2016.08.027
Files in This Item:
There are no files associated with this item.
Appears in Collections:
College of Engineering/Engineering Practice School (공과대학/대학원)Dept. of Industrial Engineering (산업공학과)Journal Papers (저널논문_산업공학과)
  • mendeley

Items in S-Space are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse