From Story Line to Box Office: A New Approach for Green-Lighting Movie Scripts

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 which 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 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 improve a studio’s gross return-on-investment significantly.