Global Reactions to COVID-19 on Twitter: A Labelled Dataset with Latent Topic, Sentiment and Emotion Attributes
- 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)
AbstractThis 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.
2020-01-28 / 2020-07-01Time Period: Tue Jan 28 00:00:00 EST 2020--Wed Jul 01 00:00:00 EDT 2020
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Update Metadata: 2021-02-17 | Issue Number: 1 | Registration Date: 2021-02-17