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COVIDSenti: a large-scale benchmark Twitter data set for COVID-19 sentiment analysis

Naseem, Usman, Razzak, Muhammad Imran, Khushi, Matloob, Eklund, Peter W and Kim, Jinman 2021, COVIDSenti: a large-scale benchmark Twitter data set for COVID-19 sentiment analysis, IEEE transactions on computational social systems, pp. 1-13, doi: 10.1109/TCSS.2021.3051189.

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Title COVIDSenti: a large-scale benchmark Twitter data set for COVID-19 sentiment analysis
Author(s) Naseem, Usman
Razzak, Muhammad ImranORCID iD for Razzak, Muhammad Imran orcid.org/0000-0002-3930-6600
Khushi, Matloob
Eklund, Peter WORCID iD for Eklund, Peter W orcid.org/0000-0003-2313-8603
Kim, Jinman
Journal name IEEE transactions on computational social systems
Start page 1
End page 13
Total pages 13
Publisher Institute of Electrical and Electronics Engineers
Place of publication Piscataway, N.J.
Publication date 2021-01-29
ISSN 2329-924X
Keyword(s) COVID-19
epidemic
misinformation
opinion mining
pandemic
sentiment analysis
text mining
Twitter
Language eng
DOI 10.1109/TCSS.2021.3051189
Indigenous content off
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30147768

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Created: Wed, 03 Feb 2021, 12:17:54 EST

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.