Work

Measuring Ideological Proportions in Political Speeches

Year: 2013

Type: conference-paper

Abstract: 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.

Source: Conference on Empirical Methods in Natural Language Processing (EMNLP)

DOI: 10.18653/v1/d13-1010

Authors: Yanchuan Sim, Brice D. L. Acree, Justin H. Gross, Noah A. Smith

Institutions: Carnegie Mellon University, University of North Carolina at Chapel Hill

Language: English

Topic: Computational and Text Analysis Methods

Subfield: General Social Sciences

Field: Social Sciences

Domain: Social Sciences

Open Access status: hybrid

Funders: National Science Foundation, Princeton University, University of Virginia

Awards: SoCS: Collaborative Research: Data-Driven, Computational Models for Discovery and Analysis of Framing, SoCS: Collaborative Research: Data-Driven, Computational Models for Discovery and Analysis of Framing