Understanding COVID-19 response by twitter users: A text analysis approach

• DOI: 10.1016/j.heliyon.2022.e09994
• Publication Date: 2022-07-19T02:44:45Z
• Authors: Digvijay Pandey; Subodh Wairya; Bandinee Pradhan; Wangmo
• Keywords: Sentiment Analysis; Thematic Analysis; Python; 2019-20 coronavirus outbreak; Pandemic

COVID-19 outbreak has caused a high number of casualties and is an unprecedented public health emergency. Twitter emerged as major platform for interactions, giving opportunity to researchers understanding response the outbreak. The analyzed 100,000 tweets with hashtags #coronavirus, #coronavirusoutbreak, #coronavirusPandemic, #COVID19, #COVID-19, #epitwitter, #ihavecorona, #StayHomeStaySafe, #TestTraceIsolate. Programming languages such Python, Google NLP, NVivo are used sentiment analysis thematic analysis. result showed 29.61% were attached positive sentiments, 29.49% mixed 23.23 % neutral sentiments 18.069% negative sentiments. Popular keywords include "cases", "home", "people" "help". We identified "30" topics categorized them into "three" themes: Public Health, around world Number Cases/Death. This study shows twitter data NLP approach can be utilized studies related discussion during Real time help reduce false messages increase efficiency in proving right guidelines people.