A machine learning approach to predict movie box-office success

Nahid Quader · Md. Osman Gani · Dipankar Chaki · Md. Haider Ali

52 citations0/0 files storedInternational Conference on Computer and Information Technology (ICCIT)

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Cinema and Media Studies Stock Market Forecasting Methods Data Mining and Machine Learning Applications

Abstract

Predicting society's reaction to a new product in the sense of popularity and adaption rate has become an emerging field of data analysis. The motion picture industry is a multi-billion-dollar business, and there is a massive amount of data related to movies is available over the internet. This study proposes a decision support system for movie investment sector using machine learning techniques. This research helps investors associated with this business for avoiding investment risks. The system predicts an approximate success rate of a movie based on its profitability by analyzing historical data from different sources like IMDb, Rotten Tomatoes, Box Office Mojo and Metacritic. Using Support Vector Machine (SVM), Neural Network and Natural Language Processing the system predicts a movie box office profit based on some pre-released features and post-released features. This paper shows Neural Network gives an accuracy of 84.1% for pre-released features and 89.27% for all features while SVM has 83.44% and 88.87% accuracy for pre-released features and all features respectively when one away prediction is considered. Moreover, we figure out that budget, IMDb votes and no. of screens are the most important features which play a vital role while predicting a movie's box-office success.

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References

1996A Parsimonious Model for Forecasting Gross Box-Office Revenues of Motion Pictures 2005Predicting box-office success of motion pictures with neural networks 2013Early Prediction of Movie Box Office Success Based on Wikipedia Activity Big Data 2016Early Predictions of Movie Success: The Who, What, and When of Profitability 2013Blogs, Advertising, and Local-Market Movie Box Office Performance 2000Predicting Movie Grosses: Winners and Losers, Blockbusters and Sleepers 2009Improving Movie Gross Prediction through News Analysis 2014Predicting movie Box-office revenues by exploiting large-scale social media content

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