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

Minhoe Hur, Pilsung Kang, Sungzoon Cho

Research output: Contribution to journalArticle

29 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)608-624
Number of pages17
JournalInformation Sciences
Volume372
DOIs
Publication statusPublished - 2016 Dec 1

Keywords

  • Box-office forecasting
  • Independent subspace model
  • Motion pictures
  • Movie reviews
  • Sentiment analysis

ASJC Scopus subject areas

  • Software
  • Control and Systems Engineering
  • Theoretical Computer Science
  • Computer Science Applications
  • Information Systems and Management
  • Artificial Intelligence

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