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metadata language: English

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

Version
5
Resource Type
Dataset : other, program source code, text
Creator
  • Gupta, Raj (Institute of High Performance Computing, A*STAR)
  • Vishwanath, Ajay (Institute of High Performance Computing (IHPC), A*STAR)
  • Yang, Yinping (Institute of High Performance Computing, A*STAR)
Publication Date
2020-09-04
Free Keywords
[COVID-19; pandemic; twitter; social media; , COVID-19; pandemic; twitter; social media; sentiment analysis; emotion recognition; ]
Description
  • Abstract

    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.
Temporal Coverage
  • 2020-01-28 / 2020-07-01
    Time Period: Tue Jan 28 00:00:00 EST 2020--Wed Jul 01 00:00:00 EDT 2020
Geographic Coverage
  • Global
Sampled Universe
Twitter posts.
Availability
Download
This study is freely available to the general public via web download.
Relations
  • Is version of
    DOI: 10.3886/E120321

Update Metadata: 2020-09-07 | Issue Number: 1 | Registration Date: 2020-09-07

Gupta, Raj; Vishwanath, Ajay; Yang, Yinping (2020): COVID-19 Twitter Dataset with Latent Topics, Sentiments and Emotions Attributes. Version: 5. ICPSR - Interuniversity Consortium for Political and Social Research. Dataset. https://doi.org/10.3886/E120321V5-44022