We present an unsupervised model for the discovery and clustering of latent "personas" (characterizations of entities).Our model simultaneously clusters documents featuring similar collections of personas.We evaluate this model on a collection of news articles about immigration, showing that personas help predict the coarse-grained framing annotations in the Media Frames Corpus.We also introduce automated model selection as a fair and robust form of feature evaluation.

We seek to measure political candidates' ideological positioning from their speeches.To accomplish this, we infer ideological cues from a corpus of political writings annotated with known ideologies.We then represent the speeches of U.S. Presidential candidates as sequences of cues and lags (filler distinguished only by its length in words).We apply a domain-informed Bayesian HMM to infer the proportions of ideologies each candidate uses in each campaign.The results are validated against a set of preregistered, domain expertauthored hypotheses.