A newer version of this project is available. See below for other available versions.

Global Reactions to COVID-19 on Twitter: A Labelled Dataset with Latent Topic, Sentiment and Emotion Attributes

Principal Investigator(s): View help for Principal Investigator(s) Raj Gupta, Institute of High Performance Computing, A*STAR; Ajay Vishwanath, Institute of High Performance Computing (IHPC), A*STAR; Yinping Yang, Institute of High Performance Computing, A*STAR

Version: View help for Version V6

Name	File Type	Size	Last Modified
Dataset-Terms of Use.pdf	application/pdf	318.8 KB	09/04/2020 07:19:AM
readme.txt	text/plain	3.2 KB	02/16/2021 04:28:PM
tweetid_userid_keyword_topics_sentiments_emotions (5k sample).csv	text/csv	696.5 KB	02/15/2021 04:43:PM
tweetid_userid_keyword_topics_sentiments_emotions (full).csv	text/csv	15.3 GB	02/15/2021 03:41:PM
tweetid_userid_keyword_topics_sentiments_emotions_timestamp (brazil sample).csv	text/csv	22.7 MB	02/14/2021 10:06:PM
tweetid_userid_keyword_topics_sentiments_emotions_timestamp (india sample).csv	text/csv	887.1 MB	02/14/2021 10:23:PM
tweetid_userid_keyword_topics_sentiments_emotions_timestamp (sg sample).csv	text/csv	34.5 MB	02/14/2021 10:02:PM
tweetid_userid_keyword_topics_sentiments_emotions_timestamp (usa sample).csv	text/csv	5.6 GB	02/14/2021 10:42:PM

Citation:

Gupta, Raj, Vishwanath, Ajay, and Yang, Yinping. Global Reactions to COVID-19 on Twitter: A Labelled Dataset with Latent Topic, Sentiment and Emotion Attributes: Twitter COVID dataset Jan 2021. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2021-02-16. https://doi.org/10.3886/E120321V6-84020

To view the citation for the overall project, see http://doi.org/10.3886/E120321V6.

Project Description

Summary: This project aims to present a large dataset for researchers to discover public conversation on Twitter surrounding the COVID-19 pandemic. As strong concerns and emotions are expressed in the publicly available tweets, we 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.

Scope of Project

Subject Terms: [COVID-19; pandemic; twitter; social media; , COVID-19; pandemic; twitter; social media; sentiment analysis; emotion recognition; ]

Geographic Coverage: Global

Universe: Twitter posts

Data Type(s): other; program source code; text

Related Publications

Download this project

Published Versions

Export Metadata

Dublin Core

DDI 2.5

Report a Problem

Found a serious problem with the data, such as disclosure risk or copyrighted content? Let us know.

This material is distributed exactly as received from the data depositor. As of April 2026, depositors are required to submit study materials in accessible formats. ICPSR has not reviewed, checked, or processed this material. For additional information about the study, please contact the investigator(s) directly. If you have questions about the accessibility of materials distributed by ICPSR or require further assistance, please visit ICPSR's Accessibility Center.