Using Crowd-Source Based Features from Social Media and Conventional Features to Predict the Movies Popularity

Abstract

Predicting the success of movies has been of interest to economists and investors (media and production houses) as well as predictive analysts. A number of attributes such as cast, genre, budget, production house, PG rating affect the popularity of a movie. Social media such as Twitter, YouTube etc. are major platforms where people can share their views about the movies. This paper describes experiments in predictive analysis using machine learning algorithms on both conventional features, collected from movies databases on Web as well as social media features (text comments on YouTube, Tweets). The results demonstrate that the sentiments harnessed from social media and other social media features can predict the success with more accuracy than that of using conventional features. We achieved best value of 77% and 61% using selected social media features for Rating and Income prediction respectively, whereas selected conventional features gave results of 76.2% and 52% respectively. More it was found that the blend of both types of attributes (conventional and those collected from social media) can outperform the existing approaches in this domain.