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Measuring Judicial Sentiment: Methods and Application to US Circuit Courts

Author

Listed:
  • Elliott Ash

    (ETH Zürich - Eidgenössische Technische Hochschule - Swiss Federal Institute of Technology [Zürich])

  • Daniel L. Chen

    (IAST - Institute for Advanced Study in Toulouse, TSE-R - Toulouse School of Economics - UT Capitole - Université Toulouse Capitole - UT - Université de Toulouse - EHESS - École des hautes études en sciences sociales - CNRS - Centre National de la Recherche Scientifique - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement, CNRS - Centre National de la Recherche Scientifique)

  • Sergio Galletta

    (University of Bergamo)

Abstract

This paper provides a general method for analysing the sentiments expressed in the language of judicial rulings. We apply natural language processing tools to the text of US appellate court opinions to extrapolate judges' sentiments (positive/good vs. negative/bad) towards a number of target social groups. We explore descriptively how these sentiments vary over time and across types of judges. In addition, we provide a method for using random assignment of judges in an instrumental variables framework to estimate causal effects of judges' sentiments. In an empirical application, we show that more positive sentiment influences future judges by increasing the likelihood of reversal but also increasing the number of forward citations.

Suggested Citation

  • Elliott Ash & Daniel L. Chen & Sergio Galletta, 2022. "Measuring Judicial Sentiment: Methods and Application to US Circuit Courts," Post-Print hal-03597819, HAL.
    • Handle: RePEc:hal:journl:hal-03597819
    DOI: 10.1111/ecca.12397
    Note: View the original document on HAL open archive server: https://hal.science/hal-03597819
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    References listed on IDEAS

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    Cited by:

    1. van Loon, Austin, 2022. "Three Families of Automated Text Analysis," SocArXiv htnej, Center for Open Science.

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