Film Quality Prediction Using Acoustic, Prosodic and Lexical Cues

Abstract

In this paper, we propose using acoustic, prosodic, and lexical features to identify movie quality as a decision support tool for film producers. Using a dataset of movie trailer audio clips paired with audience ratings for the corresponding film, we trained machine learning models to predict a film’s rating. We further analyze the impact of prosodic features with neural network feature importance approaches and find differing influence across genres. We finally compare acoustic, prosodic, and lexical feature variance against film rating, and find some evidence for an inverse association.