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Gaining Insights on U.S. Senate Speeches Using a Time Varying Text Based Ideal Point Model

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  • Paul Hofmarcher
  • Sourav Adhikari
  • Bettina Grun

Abstract

Estimating political positions of lawmakers has a long tradition in political science. We present the time varying text based ideal point model to study the political positions of lawmakers based on text data. In addition to identifying political positions, our model also provides insights into topical contents and their change over time. We use our model to analyze speeches given in the U.S. Senate between 1981 and 2017 and demonstrate how the results allow to conclude that partisanship between Republicans and Democrats increased in recent years. Further we investigate the political positions of speakers over time as well as at a specific point in time to identify speakers which are positioned at the extremes of their political party based on their speeches. The topics extracted are inspected to assess how their term compositions differ in dependence of the political position as well as how these term compositions change over time.

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  • Paul Hofmarcher & Sourav Adhikari & Bettina Grun, 2022. "Gaining Insights on U.S. Senate Speeches Using a Time Varying Text Based Ideal Point Model," Papers 2206.10877, arXiv.org.
    • Handle: RePEc:arx:papers:2206.10877
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    File URL: http://arxiv.org/pdf/2206.10877
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    1. David M. Blei & Alp Kucukelbir & Jon D. McAuliffe, 2017. "Variational Inference: A Review for Statisticians," Journal of the American Statistical Association, Taylor & Francis Journals, vol. 112(518), pages 859-877, April.
    2. Michael A. Bailey & Brian Kamoie & Forrest Maltzman, 2005. "Signals from the Tenth Justice: The Political Role of the Solicitor General in Supreme Court Decision Making," American Journal of Political Science, John Wiley & Sons, vol. 49(1), pages 72-85, January.
    3. Daniel D. Lee & H. Sebastian Seung, 1999. "Learning the parts of objects by non-negative matrix factorization," Nature, Nature, vol. 401(6755), pages 788-791, October.
    4. Laver, Michael & Benoit, Kenneth & Garry, John, 2003. "Extracting Policy Positions from Political Texts Using Words as Data," American Political Science Review, Cambridge University Press, vol. 97(2), pages 311-331, May.
    5. Matthew Gentzkow & Jesse M. Shapiro & Matt Taddy, 2019. "Measuring Group Differences in High‐Dimensional Choices: Method and Application to Congressional Speech," Econometrica, Econometric Society, vol. 87(4), pages 1307-1340, July.
    6. Adam Boche & Jeffrey B. Lewis & Aaron Rudkin & Luke Sonnet, 2018. "The new Voteview.com: preserving and continuing Keith Poole’s infrastructure for scholars, students and observers of Congress," Public Choice, Springer, vol. 176(1), pages 17-32, July.
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