  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.
