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

This dataset was created for a project that assessed Twitter data from physicians posted anonymously by administrators of a specific Twitter user page to better understand physician perspectives and sentiments about COVID-19 in the United States. Tweet identifiers are contained in the 'tweet_identifiers.csv file' Other files contain sentiment analysis data; one file used vaderSentiment in Python 3, and the other file used NRC in R (see sources below for further information and use of these packages. 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. NRC Emotion Lexicon, Saif M. Mohammad and Peter D. Turney, NRC Technical Report, December 2013, Ottawa, Canada. Jockers ML (2015). <em>Syuzhet: Extract Sentiment and Plot Arcs from Text</em>. https://github.com/mjockers/syuzhet. Code used specifically for this project may be found at: https://github.com/sullkath/tweet_analysis Link to paper publication: Pre-print in bioRxiv available at:本数据集由某研究项目构建，旨在采集某特定Twitter用户页面管理员匿名发布的医师推文数据，以深入探究美国医师针对新型冠状病毒肺炎（COVID-19）的观点与情感倾向。推文标识符存储于`tweet_identifiers.csv`文件中。其余文件包含情感分析结果：其一采用Python 3环境下的vaderSentiment工具包完成分析，其二则使用R语言中的NRC工具完成分析，有关上述工具包的详细信息与使用方法，请参见下述引用来源： [1] Hutto, C.J. 与 Gilbert, E.E.（2014）。VADER：面向社交媒体文本情感分析的简约规则模型。第八届博客与社交媒体国际会议（ICWSM-14），密歇根州安阿伯，2014年6月。 [2] NRC情感词典（NRC Emotion Lexicon），Saif M. Mohammad与Peter D. Turney，NRC技术报告，2013年12月，加拿大渥太华。 [3] Jockers ML（2015）。*Syuzhet：从文本中提取情感与情节弧*，https://github.com/mjockers/syuzhet。 本项目专用代码可于以下链接获取：https://github.com/sullkath/tweet_analysis。 论文发表相关链接：可于bioRxiv平台获取预印本，链接详见原文此处。