Article,
ASAVACT: Arabic sentiment analysis for vaccine-related COVID-19 tweets using deep learning.
S. Alhumoud, A. Wazrah, L. Alhussain, L. Alrushud, A. Aldosari, R. Altammami, N. Almukirsh, H. Alharbi, and W. Alshahrani.PeerJ Comput. Sci., (2023)
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- article
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- 2023
- journal
- PeerJ Comput. Sci.
- pages
- e1507
- volume
- 9
- ee
- https://doi.org/10.7717/peerj-cs.1507
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- http://dblp.uni-trier.de/db/journals/peerj-cs/peerj-cs9.html#AlhumoudWAAAAAAA23
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%1 journals/peerj-cs/AlhumoudWAAAAAAA23
%A Alhumoud, Sarah
%A Wazrah, Asma Al
%A Alhussain, Laila
%A Alrushud, Lama
%A Aldosari, Atheer
%A Altammami, Reema Nasser
%A Almukirsh, Njood
%A Alharbi, Hind
%A Alshahrani, Wejdan
%D 2023
%J PeerJ Comput. Sci.
%K dblp
%P e1507
%T ASAVACT: Arabic sentiment analysis for vaccine-related COVID-19 tweets using deep learning.
%U http://dblp.uni-trier.de/db/journals/peerj-cs/peerj-cs9.html#AlhumoudWAAAAAAA23
%V 9
@article{journals/peerj-cs/AlhumoudWAAAAAAA23,
added-at = {2023-12-15T00:00:00.000+0100},
author = {Alhumoud, Sarah and Wazrah, Asma Al and Alhussain, Laila and Alrushud, Lama and Aldosari, Atheer and Altammami, Reema Nasser and Almukirsh, Njood and Alharbi, Hind and Alshahrani, Wejdan},
biburl = {https://www.bibsonomy.org/bibtex/2f8ad0fdc47241e46a463357179f43ac3/dblp},
ee = {https://doi.org/10.7717/peerj-cs.1507},
interhash = {fd5d5d5e03999c62ea9d540f05ce7ff6},
intrahash = {f8ad0fdc47241e46a463357179f43ac3},
journal = {PeerJ Comput. Sci.},
keywords = {dblp},
pages = {e1507},
timestamp = {2024-04-08T17:38:39.000+0200},
title = {ASAVACT: Arabic sentiment analysis for vaccine-related COVID-19 tweets using deep learning.},
url = {http://dblp.uni-trier.de/db/journals/peerj-cs/peerj-cs9.html#AlhumoudWAAAAAAA23},
volume = 9,
year = 2023
}