Metadata record for COVID-19 Twitter Dataset with Latent Topics, Sentiments and Emotions Attributes
120321
Inter-university Consortium for Political and Social Research
ICPSR metadata records are licensed under a Creative Commons Attribution-Noncommercial 3.0 United States License.
V5
COVID-19 Twitter Dataset with Latent Topics, Sentiments and Emotions Attributes
120321
http://doi.org/10.3886/E120321V5
Raj Gupta
Ajay Vishwanath
Yinping Yang
Please see full citation.
This work is licensed under an Other license created by the data depositor. Please refer to the LICENSE file, which should be located alongside the project data and documentation.
Ann Arbor, MI: Inter-university Consortium for Political and Social Research
Gupta, Raj, Vishwanath, Ajay, and 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], 2020-09-04. https://doi.org/10.3886/E120321V5
[COVID-19
pandemic
twitter
social media
, COVID-19
pandemic
twitter
social media
sentiment analysis
emotion recognition
]
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.
Global
Twitter posts
other
program source code
text