Preprocessing Solutions for Detection of Sarcasm and Sentiment for Arabic

Mohamed Lichouri, Mourad Abbas, Besma Benaziz, Aicha Zitouni, Khaled Lounnas


Abstract
This paper describes our approach to detecting Sentiment and Sarcasm for Arabic in the ArSarcasm 2021 shared task. Data preprocessing is a crucial task for a successful learning, that is why we applied a set of preprocessing steps to the dataset before training two classifiers, namely Linear Support Vector Classifier (LSVC) and Bidirectional Long Short Term Memory (BiLSTM). The findings show that despite the simplicity of the proposed approach, using the LSVC model with a normalizing Arabic (NA) preprocessing and the BiLSTM architecture with an Embedding layer as input have yielded an encouraging F1score of 33.71% and 57.80% for sarcasm and sentiment detection, respectively.
Anthology ID:
2021.wanlp-1.49
Volume:
Proceedings of the Sixth Arabic Natural Language Processing Workshop
Month:
April
Year:
2021
Address:
Kyiv, Ukraine (Virtual)
Editors:
Nizar Habash, Houda Bouamor, Hazem Hajj, Walid Magdy, Wajdi Zaghouani, Fethi Bougares, Nadi Tomeh, Ibrahim Abu Farha, Samia Touileb
Venue:
WANLP
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
376–380
Language:
URL:
https://aclanthology.org/2021.wanlp-1.49
DOI:
Bibkey:
Cite (ACL):
Mohamed Lichouri, Mourad Abbas, Besma Benaziz, Aicha Zitouni, and Khaled Lounnas. 2021. Preprocessing Solutions for Detection of Sarcasm and Sentiment for Arabic. In Proceedings of the Sixth Arabic Natural Language Processing Workshop, pages 376–380, Kyiv, Ukraine (Virtual). Association for Computational Linguistics.
Cite (Informal):
Preprocessing Solutions for Detection of Sarcasm and Sentiment for Arabic (Lichouri et al., WANLP 2021)
Copy Citation:
PDF:
https://aclanthology.org/2021.wanlp-1.49.pdf
Data
ASTDArSarcasmArSarcasm-v2