  Our paper aims to analyze political polarization in US political system using
Language Models, and thereby help candidates make an informed decision. The
availability of this information will help voters understand their candidates
views on the economy, healthcare, education and other social issues. Our main
contributions are a dataset extracted from Wikipedia that spans the past 120
years and a Language model based method that helps analyze how polarized a
candidate is. Our data is divided into 2 parts, background information and
political information about a candidate, since our hypothesis is that the
political views of a candidate should be based on reason and be independent of
factors such as birthplace, alma mater, etc. We further split this data into 4
phases chronologically, to help understand if and how the polarization amongst
candidates changes. This data has been cleaned to remove biases. To understand
the polarization we begin by showing results from some classical language
models in Word2Vec and Doc2Vec. And then use more powerful techniques like the
Longformer, a transformer based encoder, to assimilate more information and
find the nearest neighbors of each candidate based on their political view and
their background.
