The dataset is about public conversation on Twitter surrounding the COVID-19 pandemic. They annotated seventeen latent semantic attributes for each public tweet using natural language processing techniques and machine-learning based algorithms. The latent semantic attributes include: 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. Data is accessible to people who have an OPEN ICPSR account.
This site was available on the date of the last automated link check. (2025/06/16)
Identifier | URL:https://www.openicpsr.org/openicpsr/project/120321/ |
Type | data set |
Biological Scale | population |
Collection End Date | |
Language | English |
Topics | |
Geographical Scope | Earth |
Geographical Resolution | Region, Country |
Start Date | |
End Date | |
Version | ongoing |
Accessible For Free | TRUE |
License | unspecified |
Rights | unspecified |
HTTP Status Code | 200 |
HTTP Checked On | 2025/06/16 |