Statistical Analysis of High-Level Features from State of the Union Addresses

Abstract

A computational political science approach is taken to analyze the State of the Union Addresses SUA from 1790 to 2015. While low-level features, e.g. linguistic characteristics, are commonly used for lexical analysis, the authors herein illustrate the utility of high-level features, e.g. Flesch-Kincaid readability, for knowledge discovery and discrimination between types of speeches. A process is developed and employed to exploit high-level features which employs 1 statistical clustering k-means and a literature review to define types of speeches e.g. written or oral, 2 classification methods via logistic regression to examine the validity of the defined classes, and 3 classifier-based feature selection to determine salient features. Recent interest in the SUA has posited that changes in readability in the SUA are due to declining audience capabilities; however, the authors' results show that changes in readability are a reflection of changes in the SUA delivery medium.