Measuring Ideological Proportions in Political Speeches

Yanchuan Sim · Brice D. L. Acree · Justin H. Gross · Noah A. Smith

117 citations0/0 files storedConference on Empirical Methods in Natural Language Processing (EMNLP)

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Computational and Text Analysis Methods Sentiment Analysis and Opinion Mining Cultural and political discourse analysis

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.

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1958An Economic Theory of Democracy.1990Stability in Competition 1948On the Rationale of Group Decision-making 2003Extracting Policy Positions from Political Texts Using Words as Data 2005Politicians and rhetoric: the persuasive power of metaphor 2011Language and Ideology in Congress 2011Predicting Legislative Roll Calls from Text 2008Computer-Assisted Topic Classification for Mixed-Methods Social Science Research

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2013Identifying Media Frames and Frame Dynamics Within and Across Policy Issues 2015QUOTUS 2020Understanding the Political Ideology of Legislators from Social Media Images 2023Retweet-BERT: Political Leaning Detection Using Language Features and Information Diffusion on Social Networks 2023Handbook of Computational Social Science for Policy 2016Proceedings of the First Workshop on NLP and Computational Social Science

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