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Investigating public perceptions regarding the Long COVID on Twitter using sentiment analysis and topic modeling
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作者 yu-bo fu 《Medical Data Mining》 2022年第4期56-61,共6页
Background:An estimated 10 to 30 percent of people who become infected with Severe acute respiratory syndrome coronavirus 2 will experience persistent symptoms after recovering from Coronavirus Disease 2019(COVID-19),... Background:An estimated 10 to 30 percent of people who become infected with Severe acute respiratory syndrome coronavirus 2 will experience persistent symptoms after recovering from Coronavirus Disease 2019(COVID-19),which is known as Long COVID.Social media platforms like Facebook and Twitter are the primary sources to gather and examine people’s opinion and sentiments towards various topics.Methods:In this paper,we aimed to examine sentiments,discover key themes and associated topics in Long COVID-related messages posted by Twitter users in the US between March 2022 and April 2022 using sentiment analysis and topic modeling.Results:A total of 117,789 tweets were examined,of which three dominant themes were identified,ranging from symptoms to social and economic impacts,and preventive measures.We also found that more negative sentiments were expressed in the tweets by users toward long-term COVID-19.Conclusions:Our research throws light on dominant themes,topics and sentiments surrounding the ongoing public health crisis.From the insights gained,we discuss the major implications of this study for health practitioners and policymakers. 展开更多
关键词 LONG COVID TWITTER SOCIAL media SENTIMENT analysis TOPIC modeling
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