In this paper, we discuss how machine learning could be used to produce a systematic and more objective political discourse analysis. Political footprints are vector space models (VSMs) applied to political discourse. Each of their vectors represents a word, and is produced by training the English lexicon on large text corpora. This paper presents a simple implementation of political footprints, some heuristics on how to use them, and their application to four cases: the U.N. Kyoto Protocol and Paris Agreement, and two U.S. presidential elections. The reader will be offered a number of reasons to believe that political footprints produce meaningful results, along with some suggestions on how to improve their implementation.
@article{arxiv.1705.06353,
title = {Political Footprints: Political Discourse Analysis using Pre-Trained Word Vectors},
author = {Christophe Bruchansky},
journal= {arXiv preprint arXiv:1705.06353},
year = {2017}
}