Screenplay Quality Assessment: Can We Predict Who Gets Nominated?
Ming-Chang Chiu, Tiantian Feng, Xiang Ren, Shrikanth Shri Narayanan · 2020
Deciding which scripts to turn into movies is a costly and time-consuming process for filmmakers.Thus, building a tool to aid script selection, an initial phase in movie production, can be very beneficial.Toward that goal, in this work, we present a method to evaluate the quality of a screenplay based on linguistic cues.We address this in a two-fold approach: (1) we define the task as predicting nominations of scripts at major film awards with the hypothesis that the peer-recognized scripts should have a greater chance to succeed.(2) based on industry opinions and narratology, we extract and integrate domain-specific features into common classification techniques.We face two challenges (1) scripts are much longer than other document datasets (2) nominated scripts are limited and thus difficult to collect.However, with narratology-inspired modeling and domain features, our approach offers clear improvements over strong baselines.Our work provides a new approach for future work in screenplay analysis.