Natural Language Processing
This article explores the relationship between a movie’s dialogue and its genre, leveraging domain-driven data analysis and informed feature engineering.
From fragmented speech in thrillers to expletive-laden exchanges in action movies, can we guess a movie’s genre simply by knowing its semantic and syntactic characteristics in the dialogue? If so, which ones?
We will investigate whether or not the nuanced dialogue patterns within a screenplay — its lexicon, structure, and pacing — can be powerful predictors of genre. The focus here is twofold: to leverage syntactic and semantic script characteristics as predictive features and to underscore the significance of informed feature engineering.
One of the primary gaps in many data science courses is the lack of emphasis on domain expertise and feature generation, engineering, and selection. Many courses also provide students with pre-existing datasets, and sometimes, these datasets are already cleaned. Moreover, in the workplace, the rush to produce results often overshadows the process of hypothesizing and validating predictive features, leaving little room for domain-specific exploration and understanding.
In my own experience outlined in “Using Multi-Task and Ensemble Learning to Predict Alzheimer’s Cognitive Functioning,” I witnessed the positive impact of informed feature engineering. Researching known predictors of Alzheimer’s allowed me to question the initial task and data, ultimately leading to the inclusion of key features during modeling.
In this article, I delve into a project that examines movie dialogue to illustrate my approach to research and feature extraction. The focus will be on identifying and analyzing textual, semantic, and syntactic elements within film dialogue, investigating how they interrelate, and evaluating their capacity to accurately predict a movie’s…