Abstract
As social media usage continues to rise, the demand for systems to analyze opinions and sentiments expressed in textual data has become more critical. This paper presents our submission to the Stance Detection in Arabic Language Shared Task, in which we evaluated three models: the fine-tuned MARBERT Transformer, the fine-tuned AraBERT Transformer, and an Ensemble of Machine learning Classifiers. Our findings indicate that the MARBERT Transformer outperformed the other models in performance across all targets. In contrast, the Ensemble Classifier, which combines traditional machine learning techniques, demonstrated relatively lower effectiveness.- Anthology ID:
- 2024.arabicnlp-1.97
- Volume:
- Proceedings of The Second Arabic Natural Language Processing Conference
- Month:
- August
- Year:
- 2024
- Address:
- Bangkok, Thailand
- Editors:
- Nizar Habash, Houda Bouamor, Ramy Eskander, Nadi Tomeh, Ibrahim Abu Farha, Ahmed Abdelali, Samia Touileb, Injy Hamed, Yaser Onaizan, Bashar Alhafni, Wissam Antoun, Salam Khalifa, Hatem Haddad, Imed Zitouni, Badr AlKhamissi, Rawan Almatham, Khalil Mrini
- Venues:
- ArabicNLP | WS
- SIG:
- Publisher:
- Association for Computational Linguistics
- Note:
- Pages:
- 828–831
- Language:
- URL:
- https://aclanthology.org/2024.arabicnlp-1.97
- DOI:
- 10.18653/v1/2024.arabicnlp-1.97
- Cite (ACL):
- Nouf AlShenaifi, Nourah Alangari, and Hadeel Al-Negheimish. 2024. Rasid at StanceEval: Fine-tuning MARBERT for Arabic Stance Detection. In Proceedings of The Second Arabic Natural Language Processing Conference, pages 828–831, Bangkok, Thailand. Association for Computational Linguistics.
- Cite (Informal):
- Rasid at StanceEval: Fine-tuning MARBERT for Arabic Stance Detection (AlShenaifi et al., ArabicNLP-WS 2024)
- PDF:
- https://preview.aclanthology.org/dois-2013-emnlp/2024.arabicnlp-1.97.pdf