Author: Richard J. Medford; Sameh N. Saleh; Andrew Sumarsono; Trish M. Perl; Christoph U. Lehmann
Title: An ""Infodemic"": Leveraging High-Volume Twitter Data to Understand Public Sentiment for the COVID-19 Outbreak Document date: 2020_4_7
ID: a6p6ka8w_5
Snippet: We constructed a list of hashtags related to COVID-19 to search for relevant tweets during a two-week interval from January 14 th to 28 th , 2020. We extracted the tweets using Twitter's advanced search application programing interface (API) and stored them as plain text. We identified themes and analyzed the frequency of associated keywords including infection prevention practices, vaccination, and racial prejudice. We performed a sentiment anal.....
Document: We constructed a list of hashtags related to COVID-19 to search for relevant tweets during a two-week interval from January 14 th to 28 th , 2020. We extracted the tweets using Twitter's advanced search application programing interface (API) and stored them as plain text. We identified themes and analyzed the frequency of associated keywords including infection prevention practices, vaccination, and racial prejudice. We performed a sentiment analysis using the text of tweets to identify each tweet's emotional valence (positive, negative, or neutral) [10] and predominant emotion (anger, disgust, fear, joy, sadness, or surprise) [11] . Finally, we performed topic modeling using an unsupervised machine learning method to identify and analyze related topics over time within the corpus of the tweets [12] .
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