IDEAS home Printed from https://ideas.repec.org/a/eee/irlaec/v62y2020ics0144818819303667.html

Text classification of ideological direction in judicial opinions

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0144818819303667
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.irle.2020.105903?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Hlavac, Marek, 2016. "ExtremeBounds: Extreme Bounds Analysis in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 72(i09).
    2. 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.
    3. Alberto Abadie, 2020. "Statistical Nonsignificance in Empirical Economics," American Economic Review: Insights, American Economic Association, vol. 2(2), pages 193-208, June.
    4. Xavier X. Sala-i-Martin, 1997. "I Just Ran Four Million Regressions," NBER Working Papers 6252, National Bureau of Economic Research, Inc.
    5. 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.
    6. Leamer, Edward E, 1985. "Sensitivity Analyses Would Help," American Economic Review, American Economic Association, vol. 75(3), pages 308-313, June.
    7. 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," IAST Working Papers 18-89, Institute for Advanced Study in Toulouse (IAST).
    8. Ash, Elliott & Chen, Daniel L., 2018. "Mapping the Geometry of Law using Document Embeddings," IAST Working Papers 18-77, Institute for Advanced Study in Toulouse (IAST).
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. Lauderdale, Benjamin E. & Herzog, Alexander, 2016. "Measuring Political Positions from Legislative Speech," Political Analysis, Cambridge University Press, vol. 24(3), pages 374-394, July.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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.
    2. 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.
    3. 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).
    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).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Diaf, Sami & Döpke, Jörg & Fritsche, Ulrich & Rockenbach, Ida, 2022. "Sharks and minnows in a shoal of words: Measuring latent ideological positions based on text mining techniques," European Journal of Political Economy, Elsevier, vol. 75(C).
    2. Christopher Wratil & Sara B Hobolt, 2019. "Public deliberations in the Council of the European Union: Introducing and validating DICEU," European Union Politics, , vol. 20(3), pages 511-531, September.
    3. Spruk, Rok & Kovac, Mitja, 2019. "Replicating and extending Martin-Quinn scores," International Review of Law and Economics, Elsevier, vol. 60(C).
    4. Mónica D. Oliveira & Inês Mataloto & Panos Kanavos, 2019. "Multi-criteria decision analysis for health technology assessment: addressing methodological challenges to improve the state of the art," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 20(6), pages 891-918, August.
    5. Rok Spruk & Mitja Kovac, 2018. "Inefficient Growth," Review of Economics and Institutions, Università di Perugia, vol. 9(2).
    6. Pongsak Luangaram & Yuthana Sethapramote, 2016. "Central Bank Communication and Monetary Policy Effectiveness: Evidence from Thailand," PIER Discussion Papers 20, Puey Ungphakorn Institute for Economic Research.
    7. Sami Diaf & Jörg Döpke & Ulrich Fritsche & Ida Rockenbach, 2020. "Sharks and minnows in a shoal of words: Measuring latent ideological positions of German economic research institutes based on text mining techniques," Macroeconomics and Finance Series 202001, University of Hamburg, Department of Socioeconomics.
    8. Carmen Fernandez & Eduardo Ley & Mark F. J. Steel, 2001. "Model uncertainty in cross-country growth regressions," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 16(5), pages 563-576.
    9. Carolina Ulloa-Suárez & Oscar M. Valencia & Jorge Guerra & Gustavo Sánchez, 2025. "Rules Met, Goals Missed: The Compliance–Sustainability Gap," Working Papers of BETA 2025-23, Bureau d'Economie Théorique et Appliquée, UDS, Strasbourg.
    10. Xiaohong Yu & Zhaoyang Sun, 2022. "The company they keep: When and why Chinese judges engage in collegiality," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 19(4), pages 936-1002, December.
    11. Keren Weinshall‐Margel, 2011. "Attitudinal and Neo‐Institutional Models of Supreme Court Decision Making: An Empirical and Comparative Perspective from Israel," Journal of Empirical Legal Studies, John Wiley & Sons, vol. 8(3), pages 556-586, September.
    12. C. Alper & S. Cakici, 2009. "Financial Liberalization, Fiscal Prudence and Growth: Panel Evidence from 1980–2003," Open Economies Review, Springer, vol. 20(4), pages 509-524, September.
    13. Álvaro Bustos & Tonja Jacobi, 2014. "Strategic Judicial Preference Revelation," Journal of Law and Economics, University of Chicago Press, vol. 57(1), pages 113-137.
    14. Beck, Krzysztof & Wyszyński, Mateusz & Dubel, Marcin, 2025. "Bayesian dynamic systems modelling. Bayesian model averaging for dynamic panels with weakly exogenous regressors," MPRA Paper 124689, University Library of Munich, Germany.
    15. Xavier Sala-I-Martin & Gernot Doppelhofer & Ronald I. Miller, 2004. "Determinants of Long-Term Growth: A Bayesian Averaging of Classical Estimates (BACE) Approach," American Economic Review, American Economic Association, vol. 94(4), pages 813-835, September.
    16. Hanna Bäck & Marc Debus & Wolfgang C. Müller, 2016. "Intra-party diversity and ministerial selection in coalition governments," Public Choice, Springer, vol. 166(3), pages 355-378, March.
    17. Sarel, Roee & Demirtas, Melanie, 2021. "Delegation in a multi-tier court system: Are remands in the U.S. federal courts driven by moral hazard?," European Journal of Political Economy, Elsevier, vol. 68(C).
    18. Blaise Gnimassoun & Isabelle Do Santos, 2021. "Robust structural determinants of public deficits in developing countries," Applied Economics, Taylor & Francis Journals, vol. 53(9), pages 1052-1076, February.
    19. Heijungs, Reinout & Groot, Henri L. F. de & Florax, Raymond J.G.M., 2001. "Metagrowth 1.0, a computer program for robustness analysis," Serie Research Memoranda 0031, VU University Amsterdam, Faculty of Economics, Business Administration and Econometrics.
    20. Martin Haselmayer & Marcelo Jenny, 2017. "Sentiment analysis of political communication: combining a dictionary approach with crowdcoding," Quality & Quantity: International Journal of Methodology, Springer, vol. 51(6), pages 2623-2646, November.

    More about this item

    Keywords

    ;
    ;
    ;
    ;

    JEL classification:

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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:irlaec:v:62:y:2020:i:c:s0144818819303667. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/irle .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.