A newer version of this project is available.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

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

Principal Investigator(s): View help for Principal Investigator(s) Raj Gupta, Affective Computing Group, Social and Cognitive Computing Department, Institute of High Performance Computing, A*STAR, Singapore; Ajay Vishwanath, Affective Computing Group, Social and Cognitive Computing Department, Institute of High Performance Computing (IHPC), A*STAR, Singapore & Centre for Artificial Intelligence Research, University of Agder, Norway; Yinping Yang, Affective Computing Group, Social and Cognitive Computing Department, Institute of High Performance Computing, A*STAR

Version: View help for Version V11

Name	File Type	Size	Last Modified
COVID19_twitter_full_dataset.csv.zip	application/zip	4.8 GB	09/11/2021 09:27:AM
COVID_Twitter_database_paper.pdf	application/pdf	1.4 MB	09/11/2021 08:43:AM
Dataset-Terms of Use.pdf	application/pdf	318.8 KB	09/11/2021 08:09:AM
readme.txt	text/plain	3.2 KB	09/11/2021 06:39:AM
tweetid_userid_keyword_sentiments_emotions_Argentina.csv.zip	application/zip	1.4 MB	09/11/2021 06:16:AM
tweetid_userid_keyword_sentiments_emotions_Australia.csv.zip	application/zip	76.3 MB	09/11/2021 06:14:AM
tweetid_userid_keyword_sentiments_emotions_Brazil.csv.zip	application/zip	6.2 MB	09/11/2021 05:08:AM
tweetid_userid_keyword_sentiments_emotions_Canada.csv.zip	application/zip	161 MB	09/11/2021 06:18:AM
tweetid_userid_keyword_sentiments_emotions_Colombia.csv.zip	application/zip	1.3 MB	09/11/2021 06:18:AM
tweetid_userid_keyword_sentiments_emotions_Denmark.csv.zip	application/zip	2.6 MB	09/11/2021 06:19:AM

Citation:

Gupta, Raj, Vishwanath, Ajay, and Yang, Yinping. COVID-19 Twitter Dataset with Latent Topics, Sentiments and Emotions Attributes: Twitter COVID dataset Sep2021. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2021-11-04. https://doi.org/10.3886/E120321V11-101600

To view the citation for the overall project, see http://doi.org/10.3886/E120321V11.

Project Description

Summary: This project aims to present a large dataset for researchers to discover public conversation on Twitter surrounding the COVID-19 pandemic. From 28 January 2020 to 1 September 2021, we collected over 198 million Twitter posts from more than 25 million unique users using four keywords: “corona”, “wuhan”, “nCov” and “covid”. Leveraging topic modeling techniques and pre-trained machine learning-based emotion analytic algorithms, we labeled each tweet with seventeen semantic attributes, including a) ten binary attributes indicating the tweet’s relevance or irrelevance to the top ten detected topics, b) five quantitative emotion attributes indicating the degree of intensity of the valence or sentiment (from 0: very negative to 1: very positive), and the degree of intensity of fear, anger, happiness and sadness emotions (from 0: not at all to 1: extremely intense), and c) two qualitative attributes indicating the sentiment category (very negative, negative, neutral or mixed, positive, very positive) and the dominant emotion category (fear, anger, happiness, sadness, no specific emotion) the tweet is mainly expressing.

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

Collection Notes: The latest version has data updated up to 1 Sep 2021, including additional csv downloads for 29 countries)

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 it arrived from the data depositor. ICPSR has not checked or processed this material. Users should consult the investigator(s) if further information is desired.