The promise and pitfall of automated text-scaling techniques for the analysis of jurisprudential change

I consider the potential of eight text-scaling methods for the analysis of jurisprudential change. I use a small corpus of well-documented German Federal Constitutional Court opinions on European integration to compare the machine-generated scores to scholarly accounts of the case law and legal expe...

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Bibliographic Details
Published in:Artificial intelligence and law 2021-06, Vol.29 (2), p.239-269
Main Author: Dyevre, Arthur
Format: Article
Language:English
Subjects:
Artificial Intelligence
Automation
Computational linguistics
Computer Science
Data mining
Information Storage and Retrieval
Intellectual Property
International economic integration
IT Law
Judicial opinions
Language processing
Laws, regulations and rules
Legal Aspects of Computing
Legal research
Media Law
Methods
Natural language interfaces
Original Research
Philosophy of Law
Scaling
Sentiment analysis
Stare decisis
Technology application
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Summary:I consider the potential of eight text-scaling methods for the analysis of jurisprudential change. I use a small corpus of well-documented German Federal Constitutional Court opinions on European integration to compare the machine-generated scores to scholarly accounts of the case law and legal expert ratings. Naive Bayes, Word2Vec, Correspondence Analysis and Latent Semantic Analysis appear to perform well. Less convincing are the performance of Wordscores, ML Affinity and lexicon-based sentiment analysis. While both the high-dimensionality of judicial texts and the validation of computer-based jurisprudential estimates pose major methodological challenges, I conclude that automated text-scaling methods hold out great promise for legal research.
ISSN:0924-8463
1572-8382
DOI:10.1007/s10506-020-09274-0