Text classification of ideological direction in judicial opinions

Carina Hausladen, Marcel H. Schubert and Elliott Ash

International Review of Law and Economics, 2020, vol. 62, issue C

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.

Keywords: Judge ideology; Circuit courts; Text data; NLP (search for similar items in EconPapers)
JEL-codes: C8 K0 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:irlaec:v:62:y:2020:i:c:s0144818819303667

DOI: 10.1016/j.irle.2020.105903

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