Datasets for Tweets from Anonymous Physicians about COVID-19 in the U.S.

<p>This dataset was created for a project that assessed Twitter data from physicians&nbsp;posted anonymously by administrators of a specific Twitter user page to better understand physician&nbsp;perspectives and sentiments about&nbsp;COVID-19 in the United States.&nbsp;</p> <p>Tweet identifiers are contained in the &#39;tweet_identifiers.csv file&#39;</p> <p>Other files contain sentiment analysis data; one file used&nbsp;vaderSentiment in Python 3, and the other file used&nbsp;NRC in R (see sources below for further information and use of these packages.</p> <ol> <li>Hutto, C.J. &amp; Gilbert, E.E. (2014). VADER: A Parsimonious Rule-based Model for Sentiment Analysis of Social Media Text. Eighth International Conference on Weblogs and Social Media (ICWSM-14). Ann Arbor, MI, June 2014.</li> <li>NRC Emotion Lexicon, Saif M. Mohammad and Peter D. Turney, NRC Technical Report, December 2013, Ottawa, Canada.</li> <li>Jockers ML (2015).&nbsp;<em>Syuzhet: Extract Sentiment and Plot Arcs from Text</em>.&nbsp;<a href="https://github.com/mjockers/syuzhet">https://github.com/mjockers/syuzhet</a>.</li> </ol> <p>Code used specifically for this project may be found at:&nbsp;https://github.com/sullkath/tweet_analysis</p> <p>Link to paper publication:&nbsp;</p> <p>Pre-print in bioRxiv available at:&nbsp;</p>