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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

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Global Reactions to COVID-19 on Twitter: A Labelled Dataset with Latent Topic, Sentiment and Emotion Attributes		0

Project 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. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2021-02-16. https://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

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