Political Footprints: Political Discourse Analysis using Pre-Trained Word Vectors
This provides a systematic and objective method for analyzing political discourse, which could benefit researchers and policymakers, but it appears incremental as it builds on existing word vector techniques.
The paper tackles political discourse analysis by introducing 'political footprints' as vector space models trained on large text corpora, applying them to cases like the U.N. Kyoto Protocol and U.S. presidential elections to show they produce meaningful results.
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.