Predicting the Vote Using Legislative Speech

As most dedicated observers of voting bodies like the U.S. Supreme Court can attest, it is possible to guess vote outcomes based on statements made during deliberations or questioning by the voting members. We show this is also possible to do automatically using machine learning, potentially providing a powerful tool to ordinary citizens. Our working hypothesis is that verbal utterances made during the legislative process by elected representatives can indicate their intent on a future vote, and therefore can be used to automatically predict said vote to a significant degree. In this paper, we examine thousands of hours of legislative deliberations from the California state legislature's 2015-2016 session to form models of voting behavior for each legislator and use them to train classifiers and predict the votes that occur subsequent to the discussions. We can achieve average legislator vote prediction accuracies as high as 83%. For bill vote prediction, our model can achieve 76% accuracy with an F1 score of 0.83 using a balanced dataset.