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.