Title: Mining trailer reviews for predicting ratings and box office success of upcoming movies

Authors: Debaditya Barman; Chandrai Kayal; Nirmalya Chowdhury

Addresses: Department of Computer and System Sciences, Visva-Bharati, Santiniketan, 731235, India ' Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032, India ' Department of Computer Science and Engineering, Jadavpur University, Kolkata, 700032, India

Abstract: Around 60% of the movies produced worldwide are box office failures. Since it affects a large number of stakeholders, movie business prediction is a very relevant as well as challenging problem. There had been many attempts to predict the box office earnings of a movie after the theatrical release. Comparatively research works are inadequate to predict a movie's fate before its release. Viewers are introduced to a movie via trailers before its theatrical release. The reviews of these trailers are indicative of a movie's initial success. This work is focused on movie rating and business prediction on the basis of trailer reviews as well as other attributes. Several experiments have been performed using multiple classifiers to find appropriate classifier(s) which can predict the rating and box office performance of a movie to be launched. Experimentally it has been found that random forest (RF) classifier has outperformed others and produced very promising results.

Keywords: text mining; sentiment analysis; machine learning; movie rating; opening weekend income; gross income; movie trailer; sensitivity analysis.

DOI: 10.1504/IJBIDM.2022.119946

International Journal of Business Intelligence and Data Mining, 2022 Vol.20 No.1, pp.1 - 34

Received: 18 Sep 2019
Accepted: 25 Nov 2019

Published online: 17 Dec 2021 *

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