Using the Least Squares Support Vector Regression to Forecast Movie Sales with Data from Twitter and Movie Databases

Yi-Ting Huang,Ping-Feng Pai

doi:10.3390/sym12040625

Abstract

Due to the rapid prominence and popularity of social media, social broadcasting networks with voluntary information sharing have become one of the most powerful ways to spread word-of-mouth opinions, and thus, have influence on consumers’ preferences toward products. Therefore, sentiment analysis data from social media have become more important in forecasting product sales. For the movie industry, the opinions expressed on social media have increasing impacts on movie sales. In addition, some databases, such as the Box Office Mojo and Internet Movie Database (IMDb), contain structured data for predicting movie sales. Thus, three categories of data—data of movie databases, data of tweets, and hybrid data including movies databases and tweets—are employed symmetrically in this study. The aim of this study is to employ the least squares support vector regression (LSSVR) to forecast movie sales worldwide according to these three forms of data. In addition, three other forecasting techniques—namely, the back propagation neural network (BPNN), the generalized regression neural network (GRNN), and the multivariate linear regression (MLR) model—were used to forecast movie sales with the three types of data. The empirical results show that the LSSVR model with hybrid data can obtain more accurate results than the other forecasting models with all data types. Thus, forecasting movie sales using the LSSSVR model with data containing movie databases and tweets is a feasible and prospective method to forecast movie sales.

Highlights

Due to the booming popularity of the Internet, people have become accustomed to expressing opinions through social media, which has subsequently become a crucial communication channel among consumers
This study proposed a framework using data from Twitter and movie databases to predict movie sales with several forecasting models
Genetic algorithms were employed to determine the parameters of the least squares support vector regression, the back propagation neural network, and the generalized regression neural networks

Summary

Introduction

Due to the booming popularity of the Internet, people have become accustomed to expressing opinions through social media, which has subsequently become a crucial communication channel among consumers. Integrated machine learning approaches with the independent subspace method to forecast box office performance by using the sentiments of movie reviews They found that the proposed models were able to obtain accurate and robust forecasting results for different forecasting periods. Employed machine learning-based techniques with social network service data to predict box office performance, where genetic algorithms were applied for selecting the essential input variables for the proposed models. Rui et al [20] used the dynamic panel data model, support vector machines, naive Bayesian methods, and tweets to predict the impact of word-of-mouth on movie sales, and found that its influence was proportional to the number of followers. Liu [24] studied the influence of word-of-mouth on movie box office revenues, and the numerical results revealed that the impact of word-of-mouth is relatively essential in the movie’s prerelease week and the first opening week.

The Least Square Support Vector Regression

The Proposed Architecture for Forecasting Movie Sales

Numerical Results

Average absolute percentage values obtained forecasting models

Conclusions