Date of Original Version
Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP)
Copyright 2013 Association for Computational Linguistics
Abstract or Description
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.
Proceedings of Conference on Empirical Methods in Natural Language Processing (EMNLP), 91-101.