Box office forecasting using machine learning algorithms based on SNS data

Taegu Kim, Jungsik Hong, Pilsung Kang

Research output: Contribution to journalArticle

40 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)364-390
Number of pages27
JournalInternational Journal of Forecasting
Volume31
Issue number2
DOIs
Publication statusPublished - 2015 Apr 1

Fingerprint

Learning algorithm
Machine learning
Social networks
Motion pictures
Forecasting accuracy
Genetic algorithm
Nonlinear regression
Screening

Keywords

  • Box office earning forecast
  • Forecast combination
  • Genetic algorithm
  • Machine learning
  • Social network service

ASJC Scopus subject areas

  • Business and International Management

Cite this

Box office forecasting using machine learning algorithms based on SNS data. / Kim, Taegu; Hong, Jungsik; Kang, Pilsung.

In: International Journal of Forecasting, Vol. 31, No. 2, 01.04.2015, p. 364-390.

Research output: Contribution to journalArticle

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