Sarcasm and Sentiment Detection In Arabic Tweets Using BERT-based Models and Data Augmentation

Abeer Abuzayed, Hend Al-Khalifa


Abstract
In this paper, we describe our efforts on the shared task of sarcasm and sentiment detection in Arabic (Abu Farha et al., 2021). The shared task consists of two sub-tasks: Sarcasm Detection (Subtask 1) and Sentiment Analysis (Subtask 2). Our experiments were based on fine-tuning seven BERT-based models with data augmentation to solve the imbalanced data problem. For both tasks, the MARBERT BERT-based model with data augmentation outperformed other models with an increase of the F-score by 15% for both tasks which shows the effectiveness of our approach.
Anthology ID:
2021.wanlp-1.38
Volume:
Proceedings of the Sixth Arabic Natural Language Processing Workshop
Month:
April
Year:
2021
Address:
Kyiv, Ukraine (Virtual)
Venues:
EACL | WANLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
312–317
Language:
URL:
https://aclanthology.org/2021.wanlp-1.38
DOI:
Bibkey:
Cite (ACL):
Abeer Abuzayed and Hend Al-Khalifa. 2021. Sarcasm and Sentiment Detection In Arabic Tweets Using BERT-based Models and Data Augmentation. In Proceedings of the Sixth Arabic Natural Language Processing Workshop, pages 312–317, Kyiv, Ukraine (Virtual). Association for Computational Linguistics.
Cite (Informal):
Sarcasm and Sentiment Detection In Arabic Tweets Using BERT-based Models and Data Augmentation (Abuzayed & Al-Khalifa, WANLP 2021)
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PDF:
https://aclanthology.org/2021.wanlp-1.38.pdf