  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.
