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.

通过使用语言模型分析美国政治体系中的政治极化，我们提供了对候选人立场的分析信息，以帮助选民了解其在经济、医疗保健、教育和其他社会问题上的观点，并进一步分析候选人的数据集和使用 “Longformer” 这样更强大的方式，找到每个候选人在其政治观点和背景上的最近邻。