From story line to box office: A new approach for green-lighting movie scripts

Abstract

Movie studios often have to choose among thousands of scripts to decide which ones to turn into movies. Despite the huge amount of money at stake, this process - known as green-lighting in the movie industry - is largely a guesswork based on experts' experience and intuitions. In this paper, we propose a new approach to help studios evaluate scripts that will then lead to more profitable green-lighting decisions. Our approach combines screenwriting domain knowledge, natural-language processing techniques, and statistical learning methods to forecast a movie's return on investment (ROI) based only on textual information available in movie scripts. We test our model in a holdout decision task to show that our model is able to significantly improve a studio's gross ROI. © 2007 INFORMS.