A Bayesian Mixed Effects Model of Literary Character
David Bamman, Ted Underwood, Noah A. Smith · 2014
We consider the problem of automatically inferring latent character types in a collection of 15,099 English novels published between 1700 and 1899.Unlike prior work in which character types are assumed responsible for probabilistically generating all text associated with a character, we introduce a model that employs multiple effects to account for the influence of extra-linguistic information (such as author).In an empirical evaluation, we find that this method leads to improved agreement with the preregistered judgments of a literary scholar, complementing the results of alternative models.