COVID-19 Twitter Dataset with Latent Topics, Sentiments and Emotions Attributes

Resource Type
Dataset : other, program source code, text
  • Gupta, Raj (Institute of High Performance Computing, A*STAR)
  • Vishwanath, Ajay (Institute of High Performance Computing (IHPC), A*STAR)
  • Yang, Yinping (Institute of High Performance Computing, A*STAR)
Publication Date
Funding Reference
  • Agency for Science, Technology and Research (A*STAR)
    • Award Number: ETPL/18-GAP050-R20A
  • Singapore Ministry of Health’s National Medical Research Council
    • Award Number: COVID19RF-005
  • Institute of High Performance Computing, A*STAR
Free Keywords
[COVID-19; pandemic; twitter; social media; , COVID-19; pandemic; twitter; social media; sentiment analysis; emotion recognition; ]
  • Abstract

    This 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.
Temporal Coverage
  • 2020-01-28 / 2020-07-01
    Time Period: Tue Jan 28 00:00:00 EST 2020--Wed Jul 01 00:00:00 EDT 2020
Geographic Coverage
  • Global
Sampled Universe
Twitter posts.
  • Is version of
    DOI: 10.3886/E120321
  • “Global Sentiments Surrounding the COVID-19 Pandemic on Twitter: Analysis of Twitter Trends.” JMIR Public Health Surveill, n.d.

Update Metadata: 2020-07-23 | Issue Number: 1 | Registration Date: 2020-07-23