English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

In this paper, we discuss how machine learning could be used to produce asystematic and more objective political discourse analysis. Politicalfootprints are vector space models (VSMs) applied to political discourse. Eachof their vectors represents a word, and is produced by training the Englishlexicon on large text corpora. This paper presents a simple implementation ofpolitical footprints, some heuristics on how to use them, and their applicationto 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 tobelieve that political footprints produce meaningful results, along with somesuggestions on how to improve their implementation.

Political Footprints: Political Discourse Analysis using Pre-Trained Word Vectors