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

Autor:	Luu, Truong, Follmann, Rosangela
Rok vydání:	2022
Předmět:	Computer Science - Computers and Society Computer Science - Computation and Language Computer Science - Social and Information Networks
Druh dokumentu:	Working Paper
DOI:	10.1177/01655515211068167
Popis:	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. Comment: 26 pages, 11 figures. Journal Information Science, 2022
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/2202.01708 Zobrazit plný text záznamu View this record from Arxiv