Search Conferences

Type in any word, words or author name. This searchs through the abstract title, keywords and abstract text and authors. You may search all conferences or just select one conference.

 All Conferences
 EMAC 2019 Annual Conference
 EMAC 2020 Annual Conference
 EMAC 2020 Regional Conference
 EMAC 2021 Annual Conference
 EMAC 2021 Regional Conference
 EMAC 2022 Annual
 EMAC 2022 Regional Conference

EMAC 2020 Annual Conference

Which Scripts Are More Profitable?: A Topic Model Analysis for the Movie’s Green-lighting process

Published: May 27, 2020


Jongdae Kim, Seoul National University; Youseok Lee, Myongji University; Inseong Song, Seoul National University


New Product Development; Entertainment Industry; Latent Dirichlet Allocation


Starting a new movie production is risky. The budget is huge and the profitability of the box office varies largely across movies. Within the same context, although the process to decide which scripts are allowed to move forward to pre-production, known as the green-lighting process, is important for movie studios, this has a lot of difficulties because there are few empirical evidence so that they have to depend on experts’ experiences. In this paper, we develop an empirical approach based on text mining to evaluate movie scripts’ profitability. First, we use latent Dirichlet allocation to find latent topics from scripts and extract probabilities of scripts assigned to each topic as an explanatory variable. From the regression and the classification based on a movie’s return-on-investment, we find the effect of probabilities of specific topics are significant. We expect our model could help studios to make more profitable and systematic decisions in the green-lighting process.