Open Access
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volume 36
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issue 10
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pages 102264
On the robustness of arabic aspect-based sentiment analysis: A comprehensive exploration of transformer-based models
Publication type: Journal Article
Publication date: 2024-12-11
scimago Q1
wos Q1
SJR: 1.357
CiteScore: 15.8
Impact factor: 6.1
ISSN: 13191578, 22131248
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AlMasaud A., Al-Baity H. H. On the robustness of arabic aspect-based sentiment analysis: A comprehensive exploration of transformer-based models // Journal of King Saud University - Computer and Information Sciences. 2024. Vol. 36. No. 10. p. 102264.
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AlMasaud A., Al-Baity H. H. On the robustness of arabic aspect-based sentiment analysis: A comprehensive exploration of transformer-based models // Journal of King Saud University - Computer and Information Sciences. 2024. Vol. 36. No. 10. p. 102264.
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TY - JOUR
DO - 10.1016/j.jksuci.2024.102264
UR - https://linkinghub.elsevier.com/retrieve/pii/S1319157824003537
TI - On the robustness of arabic aspect-based sentiment analysis: A comprehensive exploration of transformer-based models
T2 - Journal of King Saud University - Computer and Information Sciences
AU - AlMasaud, Alanod
AU - Al-Baity, Heyam H.
PY - 2024
DA - 2024/12/11
PB - Springer Nature
SP - 102264
IS - 10
VL - 36
SN - 1319-1578
SN - 2213-1248
ER -
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BibTex (up to 50 authors)
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@article{2024_AlMasaud,
author = {Alanod AlMasaud and Heyam H. Al-Baity},
title = {On the robustness of arabic aspect-based sentiment analysis: A comprehensive exploration of transformer-based models},
journal = {Journal of King Saud University - Computer and Information Sciences},
year = {2024},
volume = {36},
publisher = {Springer Nature},
month = {dec},
url = {https://linkinghub.elsevier.com/retrieve/pii/S1319157824003537},
number = {10},
pages = {102264},
doi = {10.1016/j.jksuci.2024.102264}
}
Cite this
MLA
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AlMasaud, Alanod, and Heyam H. Al-Baity. “On the robustness of arabic aspect-based sentiment analysis: A comprehensive exploration of transformer-based models.” Journal of King Saud University - Computer and Information Sciences, vol. 36, no. 10, Dec. 2024, p. 102264. https://linkinghub.elsevier.com/retrieve/pii/S1319157824003537.