Movie Popularity Classification based on Inherent Movie Attributes using C4.5,PART and Correlation Coefficient

TL;DR

This paper proposes a method to classify movie popularity before release using inherent attributes and machine learning algorithms, addressing the challenge of high-dimensional attribute data.

Contribution

It introduces a classification scheme for pre-release movie popularity based on attributes using C4.5 and PART algorithms, and analyzes attribute relations with correlation coefficients.

Findings

Effective classification of movie popularity using inherent attributes.

Identification of attribute relations through correlation analysis.

Potential for predicting movie success before release.

Abstract

Abundance of movie data across the internet makes it an obvious candidate for machine learning and knowledge discovery. But most researches are directed towards bi-polar classification of movie or generation of a movie recommendation system based on reviews given by viewers on various internet sites. Classification of movie popularity based solely on attributes of a movie i.e. actor, actress, director rating, language, country and budget etc. has been less highlighted due to large number of attributes that are associated with each movie and their differences in dimensions. In this paper, we propose classification scheme of pre-release movie popularity based on inherent attributes using C4.5 and PART classifier algorithm and define the relation between attributes of post release movies using correlation coefficient.