  We present a novel approach to the measurement of American state legislature
polarization with an experimental comparison of three different machine
learning algorithms. Our approach strictly relies on public data sources and
open source software. The results suggest that artificial neural network
regression has the best outcome compared to both support vector machine and
ordinary least squares regression in the prediction of both state House and
state Senate legislature polarization. In addition to the technical outcomes of
our study, broader implications are assessed as a means of highlighting the
importance of accessible information for the higher purpose of promoting civic
responsibility.
