Political Rhetoric Through the Lens of Non-Parametric Statistics: Are Our Legislators that Different?

We present a novel statistical analysis of legislative rhetoric in the US Senate that sheds a light on hidden patterns in the behaviour of Senators as a function of their time in office. Using natural language processing, we create a novel comprehensive data set based on the speeches of all Senators who served on the US Senate Committee on Energy and Natural Resources in 2001-2011. We develop a new measure of congressional speech, based on Senators' attitudes towards the dominant energy interests. To evaluate intrinsically dynamic formation of groups among Senators, we adopt a model-free unsupervised space-time data mining algorithm that has been proposed in the context of tracking dynamic clusters in environmental georeferenced data streams. Our approach based on a two-stage hybrid supervised-unsupervised learning methodology is innovative and data driven and transcends conventional disciplinary borders. We discover that legislators become much more alike after the first few years of their term, regardless of their partisanship and campaign promises.