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Sequel movie revenue prediction model based on sentiment analysis
Data Technologies and Applications ( IF 1.6 ) Pub Date : 2020-10-08 , DOI: 10.1108/dta-10-2019-0180
Ibrahim Said Ahmad , Azuraliza Abu Bakar , Mohd Ridzwan Yaakub , Mohammad Darwich

Purpose

Sequel movies are very popular; however, there are limited studies on sequel movie revenue prediction. The purpose of this paper is to propose a sentiment analysis based model for sequel movie revenue prediction and to propose a missing value imputation method for the sequel revenue prediction dataset.

Design/methodology/approach

A sequel of a successful movie will most likely also be successful. Therefore, we propose a supervised learning approach in which data are created from sequel movies to predict the box-office revenue of an upcoming sequel. The algorithms used in the prediction are multiple linear regression, support vector machine and multilayer perceptron neural network.

Findings

The results show that using four sequel movies in a franchise to predict the box-office revenue of a fifth sequel achieved better prediction than using three sequels, which was also better than using two sequel movies.

Research limitations/implications

The model produced will be beneficial to movie producers and other stakeholders in the movie industry in deciding the viability of producing a movie sequel.

Originality/value

Previous studies do not give priority to sequel movies in movie revenue prediction. Additionally, a new missing value imputation method was introduced. Finally, sequel movie revenue prediction dataset was prepared.



中文翻译:

基于情感分析的续集电影收入预测模型

目的

续集电影非常受欢迎。但是,关于续集电影收入预测的研究很少。本文的目的是提出一种基于情感分析的续集电影收入预测模型,并提出续集收入预测数据集的缺失值估算方法。

设计/方法/方法

成功电影的续集也很可能会成功。因此,我们提出了一种监督学习的方法,其中从续集电影中创建数据以预测即将上映的续集的票房收入。预测中使用的算法是多元线性回归,支持向量机和多层感知器神经网络。

发现

结果表明,使用一部特许经营权的四部续集电影来预测第五部续集的票房收入要比使用三部续集来的电影要好,这也比使用两部续集电影的要好。

研究局限/意义

所产生的模型将对电影制作人和电影业中的其他利益相关者在决定制作电影续集的可行性方面有益。

创意/价值

先前的研究在电影收入预测中没有优先考虑续集电影。此外,引入了一种新的缺失值估算方法。最后,准备了续集电影收入预测数据集。

更新日期:2020-11-02
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