Forecasting film audience ratings: A natural language processing approach to script and production data
Karl Cini, John Abela · Entertainment Computing · 2025
The film industry is an important entertainment avenue for audiences of all ages. Demand for good quality scripts remains a core element of this industry, rendering the screenplay a pivotal tool at the green lighting stage. While previous work addressed isolated elements influencing the performance of a movie, this research aims to bring together known influential factors and some novel approaches by applying Natural Language Processing (NLP) and Machine Learning (ML) techniques to analyse movie scripts, with the aim of extracting valuable insights and patterns that are able to predict the audience rating as collated by the Internet Movie Database (IMDb). This research helps producers determine which movies are most viable for financing. By providing a sound method to sift through and rank the various script projects presented to them, they can focus on scripts that are likely to perform better. Methods adopted in this research include the use of lexicons for the extraction of linguistic features, the analysis of emotional arcs in movies, embedding strategies for the script and statistical features generated from sentiment analysis. These features are concatenated to cast and crew specific factors to train various regression models by using a forward rolling window training strategy. • Developed Machine Learning model predicts movie audience ratings from scripts. • Integrated Natural Language Processing features like emotional arcs and script embeddings. • Achieved R 2 of 0.5255 and 84.76% one-away accuracy. • Utilised time-aware rolling window validation to prevent data leakage.