Prediction of box-office success: A review of trends and machine learning computational models

The movie industry is faced with high uncertainty owing to the challenges businesses have in forecasting sales and revenues. The huge upfront investments associated with the movie industry require investments to be informed by reliable methods of predicting success or returns from their investments. The study sets to identify the best forecasting techniques for box-office products. Previous studies focused on predicting box-office success using pre-release and post-release during and after the production phase. The study focused on reviewing existing literature in predicting box-office success with the ultimate goal of determining the most frequently used prediction algorithm(s), dataset source and their accuracy results. We applied the PRISMA model to review published papers from 2010 to 2019 extracted from Google Scholar, Science Direct, IEEE Xplore Digital Library, ACM Digital Library and Springer Link. The study shows that the support vector machine was frequently used to predict box-office success with 21.74% followed by linear regression with 17.39% of total frequency contribution. The study also reviewed that Internet Movie Database (IMDb) is the most used box-office dataset source with 40.741% of the total frequency followed by Wikipedia with 11.111%.