# The relationship between sentiment score and COVID-19 cases in the United States

arXiv: https://arxiv.org/abs/2202.01708 · HTML: https://mlsanity.com/paper/2202.01708/

## Key facts

- No citing papers in the map yet (data through 7 Oct 2026).
- Builds on 1 of its 57 references that are in the map.
- First posted on arXiv 9 Jan 2022.

## Builds on (most cited first, 1 of 1)

- [COVID-19 Public Sentiment Insights and Machine Learning for Tweets Classification](https://mlsanity.com/paper/2005.10898/) (2020)

## Abstract (arXiv)

The coronavirus disease (COVID-19) continues to have devastating effects across the globe. No nation has been free from the uncertainty brought by this pandemic. The health, social and economic tolls associated with it are causing strong emotions and spreading fear in people of all ages, genders, and races. Since the beginning of the COVID-19 pandemic, many have expressed their feelings and opinions related to a wide range of aspects of their lives via Twitter. In this study, we consider a framework for extracting sentiment scores and opinions from COVID-19 related tweets. We connect users' sentiment with COVID-19 cases across the USA and investigate the effect of specific COVID-19 milestones on public sentiment. The results of this work may help with the development of pandemic-related legislation, serve as a guide for scientific work, as well as inform and educate the public on core issues related to the pandemic.