Movie Popularity Classification based on Inherent Movie Attributes using C4.5,PART and Correlation Coefficient
This work addresses movie studios or marketers by providing a tool for predicting popularity without relying on viewer reviews, though it is incremental as it applies existing methods to a less-explored dataset.
The paper tackles the problem of classifying movie popularity based solely on inherent attributes like actor, director, and budget, using C4.5 and PART classifiers for pre-release prediction and correlation coefficients for post-release analysis, achieving results that address a gap in existing research focused on reviews or bipolar classification.
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.