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Text classification of ideological direction in judicial opinions

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  • Hausladen, Carina I.
  • Schubert, Marcel H.
  • Ash, Elliott

Abstract

This paper draws on machine learning methods for text classification to predict the ideological direction of decisions from the associated text. Using a 5% hand-coded sample of cases from U.S. Circuit Courts, we explore and evaluate a variety of machine classifiers to predict “conservative decision” or “liberal decision” in held-out data. Our best classifier is highly predictive (F1 = .65) and allows us to extrapolate ideological direction to the full sample. We then use these predictions to replicate and extend Landes and Posner’s (2009) analysis of how the party of the nominating president influences circuit judge's votes.

Suggested Citation

  • Hausladen, Carina I. & Schubert, Marcel H. & Ash, Elliott, 2020. "Text classification of ideological direction in judicial opinions," International Review of Law and Economics, Elsevier, vol. 62(C).
    • Handle: RePEc:eee:irlaec:v:62:y:2020:i:c:s0144818819303667
    DOI: 10.1016/j.irle.2020.105903
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    References listed on IDEAS

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    1. Alberto Abadie, 2020. "Statistical Nonsignificance in Empirical Economics," American Economic Review: Insights, American Economic Association, vol. 2(2), pages 193-208, June.
    2. A. Colin Cameron & Jonah B. Gelbach & Douglas L. Miller, 2011. "Robust Inference With Multiway Clustering," Journal of Business & Economic Statistics, Taylor & Francis Journals, vol. 29(2), pages 238-249, April.
    3. Benjamin E. Lauderdale & Tom S. Clark, 2014. "Scaling Politically Meaningful Dimensions Using Texts and Votes," American Journal of Political Science, John Wiley & Sons, vol. 58(3), pages 754-771, July.
    4. Ash, Elliott & Chen, Daniel L. & Lu, Wei, 2018. "Motivated Reasoning in the Field: Partisanship in Precedent, Prose, Vote, and Retirement in U.S. Circuit Courts, 1800-2013," TSE Working Papers 18-976, Toulouse School of Economics (TSE).
    5. Ash, Elliott & Chen, Daniel L., 2018. "Mapping the Geometry of Law using Document Embeddings," TSE Working Papers 18-935, Toulouse School of Economics (TSE).
    6. Hlavac, Marek, 2016. "ExtremeBounds: Extreme Bounds Analysis in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 72(i09).
    7. Xavier X. Sala-i-Martin, 1997. "I Just Ran Four Million Regressions," NBER Working Papers 6252, National Bureau of Economic Research, Inc.
    8. Jonathan B. Slapin & Sven‐Oliver Proksch, 2008. "A Scaling Model for Estimating Time‐Series Party Positions from Texts," American Journal of Political Science, John Wiley & Sons, vol. 52(3), pages 705-722, July.
    9. 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.
    10. Segal, Jeffrey A. & Cover, Albert D., 1989. "Ideological Values and the Votes of U.S. Supreme Court Justices," American Political Science Review, Cambridge University Press, vol. 83(2), pages 557-565, June.
    11. Lauderdale, Benjamin E. & Herzog, Alexander, 2016. "Measuring Political Positions from Legislative Speech," Political Analysis, Cambridge University Press, vol. 24(3), pages 374-394, July.
    12. Martin, Andrew D. & Quinn, Kevin M., 2002. "Dynamic Ideal Point Estimation via Markov Chain Monte Carlo for the U.S. Supreme Court, 1953–1999," Political Analysis, Cambridge University Press, vol. 10(2), pages 134-153, April.
    13. Leamer, Edward E, 1985. "Sensitivity Analyses Would Help," American Economic Review, American Economic Association, vol. 75(3), pages 308-313, June.
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    Cited by:

    1. Baumann, Florian & Fagan, Frank, 2023. "When more isn’t always better: The ambiguity of fully transparent judicial action and unrestricted publication rules," International Review of Law and Economics, Elsevier, vol. 75(C).
    2. Renáta Németh, 2023. "A scoping review on the use of natural language processing in research on political polarization: trends and research prospects," Journal of Computational Social Science, Springer, vol. 6(1), pages 289-313, April.
    3. Henrika Langen, 2022. "The Impact of the #MeToo Movement on Language at Court -- A text-based causal inference approach," Papers 2209.00409, arXiv.org, revised Sep 2023.
    4. Yıldırım, Engin & Sert, Mehmet Fatih & Kartal, Burcu & Çalış, Şuayyip, 2023. "Non-compliance of the European Court of Human Rights decisions: A machine learning analysis," International Review of Law and Economics, Elsevier, vol. 76(C).

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    JEL classification:

    • C8 - Mathematical and Quantitative Methods - - Data Collection and Data Estimation Methodology; Computer Programs
    • K0 - Law and Economics - - General

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