Box office forecasting using machine learning algorithms based on SNS data

Abstract

We propose a novel approach to the box office forecasting of motion pictures using social network service (SNS) data and machine learning-based algorithms. We begin by providing a comprehensive survey of the forecasting algorithms and explanatory variables used in the motion picture domain. Because of the importance of forecasting in early periods, we develop three sequential forecasting models for predicting the non-cumulative and cumulative box office earnings: (1) prior to, (2) a week after, and (3) two weeks after release. The numbers of SNS mentions and their weekly trends are used as input variables in addition to the screening-related information. A genetic algorithm is adopted for determining significant input variables, whereas three machine learning-based nonlinear regression algorithms and their combinations are employed for building forecasting models. Experimental results show that the utilization of SNS data, machine learning-based algorithms and their combination made noticeable improvements to the forecasting accuracies of all the three models.