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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
Twitter-COVID-dataset---Sep2021			09/11/2021 01:41:PM

Project Citation:

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

Time Period(s): 1/28/2020 – 9/1/2021

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)

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