Quantifying polarization across political groups on key policy issues using sentiment analysis
Dennies Bor, Benjamin Seiyon Lee, Edward J. Oughton

TL;DR
This study uses sentiment analysis of Twitter data to quantitatively assess political polarization on key policy issues among US congress members, revealing which topics are most and least polarized.
Contribution
It introduces a novel quantitative method combining sentiment analysis and voting records to measure political polarization on specific policy issues.
Findings
Gun control is the most polarized issue among the studied policies.
Immigration and Ukraine-Russia are also highly polarized.
Taiwan, LGBTQ, and Chinese Communist Party issues show low polarization.
Abstract
There is growing concern that over the past decade, industrialized democratic nations are becoming increasingly politically polarized. Indeed, elections in the US, UK, France, and Germany have all seen tightly won races, with notable examples including the 2016 Trump vs. Clinton presidential election and the UK's Brexit referendum. However, while there has been much qualitative discussion of polarization on key issues, there are few examples of formal quantitative assessments examining this topic. Therefore, in this paper, we undertake a statistical evaluation of political polarization for representatives elected to the US congress on key policy issues between 2021-2022. The method is based on applying sentiment analysis to Twitter data and developing quantitative analysis for six political groupings defined based on voting records. Two sets of policy groups are explored, including…
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Taxonomy
TopicsSocial Media and Politics · Sentiment Analysis and Opinion Mining · Computational and Text Analysis Methods
