The advantages of employing text analysis to uncover policy positions, generate legal predictions, and inform or evaluate reform practices are multifold. Given the far-reaching effects of legislation at all levels of society these insights and their continued improvement are impactful. This research explores the use of natural language processing (NLP) and machine learning to predictively model U.S. Supreme Court case outcomes based on textual case facts. The final model achieved an F1-score of .324 and an AUC of .68. This suggests that the model can distinguish between the two target classes; however, further research is needed before machine learning models are used in the Supreme Court.
Lockard, Katherine; Slater, Robert; and Sucrese, Brandon
"Using NLP to Model U.S. Supreme Court Cases,"
SMU Data Science Review: Vol. 7:
1, Article 4.
Available at: https://scholar.smu.edu/datasciencereview/vol7/iss1/4