Universität zu Köln

Hausladen, Carina, I, Schubert, Marcel H. ORCID: 0000-0002-8739-4852 and Ash, Elliott (2020). Text classification of ideological direction in judicial opinions. Int. Rev. Law Econ., 62. NEW YORK: ELSEVIER SCIENCE INC. ISSN 1873-6394

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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. (C) 2020 Published by Elsevier Inc.

Item Type:	Journal Article
Creators:	Creators Email ORCID ORCID Put Code Hausladen, Carina, I UNSPECIFIED UNSPECIFIED UNSPECIFIED Schubert, Marcel H. UNSPECIFIED orcid.org/0000-0002-8739-4852 UNSPECIFIED Ash, Elliott UNSPECIFIED UNSPECIFIED UNSPECIFIED
URN:	urn:nbn:de:hbz:38-332196
DOI:	10.1016/j.irle.2020.105903
Journal or Publication Title:	Int. Rev. Law Econ.
Volume:	62
Date:	2020
Publisher:	ELSEVIER SCIENCE INC
Place of Publication:	NEW YORK
ISSN:	1873-6394
Language:	English
Faculty:	Faculty of Mathematics and Natural Sciences
Divisions:	Faculty of Mathematics and Natural Sciences > Department of Chemistry > Institute of Physical Chemistry
Subjects:	no entry
Uncontrolled Keywords:	Keywords Language DECISION-MAKING; LANGUAGE; POSITIONS; VALUES; COURTS; POLICY Multiple languages Economics; Law Multiple languages
Refereed:	Yes
URI:	http://kups.ub.uni-koeln.de/id/eprint/33219