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

Name: COVID-19 Twitter Dataset with Latent Topics, Sentiments and Emotions Attributes
Published: 2020-07-18
License: https://creativecommons.org/licenses/by/4.0

Principal Investigator(s): View help for Principal Investigator(s) Yinping Yang, Dr.

Version: View help for Version V1

Name	File Type	Size	Last Modified

Project Citation:

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], 2020-07-18. https://doi.org/10.3886/E120321V1

Project Description

Summary: We collected and processed a dataset and make it available for the research community to study the COVD-19 pandemic in multiple possibilities.

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 – 7/1/2020

Universe: Twitter posts

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

Collection Notes: This resource describes a large dataset covering over 63 million coronavirus-related Twitter posts from more than 13 million unique users 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. To illustrate how the dataset can be used, we present descriptive statistics around the topics, sentiments and emotions attributes and their temporal distributions, and discuss possible applications in communication, psychology, public health, economics and epidemiology.

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