Abstract:
Political discourse in the United States is becoming increasingly polarized. A
Natural Language Processing (NLP) framework is provided to uncover political
polarization, utilizing political blogs and political websites as unique sources of insight
into this phenomenon. These aspects of polarization are quantified utilizing different
machine learning classifiers and methods of analysis. The creation of an ensemble learner
as a voting system is proposed for increased accuracy of text classification of documents
between differing political poles. Methods are applied to study polarization across eleven
differing political sources from the timeline of January 2020 to July 2020. Findings
indicate that discussion of events across this timeline are highly polarized politically, and
a list of the top identifying features (words) which may indicate significant slant is
provided. This work contributes to a deeper understanding of computational methods for
studying polarization in political news media and how differing political groups manifest
in language.
