All the World's a Stage: Learning Character Models from Film

Abstract

Many forms of interactive digital entertainment involve interacting with virtual dramatic characters. Our long term goal is to procedurally generate character dialogue behavior that automatically mimics, or blends, the style of existing characters. In this paper, we show how linguistic elements in character dialogue can define the style of characters in our RPG SpyFeet. We utilize a corpus of 862 film scripts from the IMSDb website, representing 7,400 characters, 664,000 lines of dialogue and 9,599,000 word tokens. We utilize counts of linguistic reflexes that have been used previously for personality or author recognition to discriminate different character types. With classification experiments, we show that different types of characters can be distinguished at accuracies up to 83% over a baseline of 20%. We discuss the characteristics of the learned models and show how they can be used to mimic particular film characters.