2024-03-28T19:32:00
120321
Thu Mar 28 19:32:03 EDT 2024
COVID-19 Twitter Dataset with Latent Topics, Sentiments and Emotions Attributes
Raj Gupta
Ajay Vishwanath
Yinping Yang
120321
https://doi.org/10.3886/E120321V5
2020-09-04
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.
COVID-19
pandemic
twitter
social media
COVID-19
pandemic
twitter
social media
sentiment analysis
emotion recognition
Global
1/28/2020 – 7/1/2020
other
program source code
text