COVID-19 Twitter Dataset with Latent Topics, Sentiments and Emotions Attributes
- Gupta, Raj (Institute of High Performance Computing, A*STAR)
- Yang, Yinping (Institute of High Performance Computing, A*STAR)
- Vishwanath, Ajay (Institute of High Performance Computing (IHPC), A*STAR)
AbstractThis project presents a large dataset covering over 63 million coronavirus-related Twitter posts from more than 13 million unique users during since 28 January to 1 July 2020. As strong concerns and emotions are expressed in the tweets, we analyzed the tweets content using natural language processing techniques and machine-learning based algorithms, and inferred seventeen latent semantic attributes associated with each tweet, including 1) ten attributes indicating the tweet’s relevance to ten detected topics, 2) five quantitative attributes indicating the degree of intensity in the valence (i.e., unpleasantness/pleasantness) and emotional intensities across four primary emotions of fear, anger, sadness and joy, and 3) two qualitative attributes indicating the sentiment category and the most dominant emotion category, respectively.
2020-01-28 / 2020-07-01Time Period: Tue Jan 28 00:00:00 EST 2020--Wed Jul 01 00:00:00 EDT 2020
Is version of
“Global Sentiments Surrounding the COVID-19 Pandemic on Twitter: Analysis of Twitter Trends.” JMIR Public Health Surveill, n.d.
Update Metadata: 2020-07-18 | Issue Number: 1 | Registration Date: 2020-07-18