Advancing Arabic Sentiment Analysis: ArSen Benchmark and the Improved Fuzzy Deep Hybrid Network

Yang Fang, Cheng Xu, Shuhao Guan, Nan Yan, Yuke Mei


Abstract
Sentiment analysis is pivotal in Natural Language Processing for understanding opinions and emotions in text. While advancements in Sentiment analysis for English are notable, Arabic Sentiment Analysis (ASA) lags, despite the growing Arabic online user base. Existing ASA benchmarks are often outdated and lack comprehensive evaluation capabilities for state-of-the-art models. To bridge this gap, we introduce ArSen, a meticulously annotated COVID-19-themed Arabic dataset, and the IFDHN, a novel model incorporating fuzzy logic for enhanced sentiment classification. ArSen provides a contemporary, robust benchmark, and IFDHN achieves state-of-the-art performance on ASA tasks. Comprehensive evaluations demonstrate the efficacy of IFDHN using the ArSen dataset, highlighting future research directions in ASA.
Anthology ID:
2024.conll-1.39
Volume:
Proceedings of the 28th Conference on Computational Natural Language Learning
Month:
November
Year:
2024
Address:
Miami, FL, USA
Editors:
Libby Barak, Malihe Alikhani
Venue:
CoNLL
SIG:
Publisher:
Association for Computational Linguistics
Note:
Pages:
507–516
Language:
URL:
https://aclanthology.org/2024.conll-1.39
DOI:
10.18653/v1/2024.conll-1.39
Bibkey:
Cite (ACL):
Yang Fang, Cheng Xu, Shuhao Guan, Nan Yan, and Yuke Mei. 2024. Advancing Arabic Sentiment Analysis: ArSen Benchmark and the Improved Fuzzy Deep Hybrid Network. In Proceedings of the 28th Conference on Computational Natural Language Learning, pages 507–516, Miami, FL, USA. Association for Computational Linguistics.
Cite (Informal):
Advancing Arabic Sentiment Analysis: ArSen Benchmark and the Improved Fuzzy Deep Hybrid Network (Fang et al., CoNLL 2024)
Copy Citation:
PDF:
https://aclanthology.org/2024.conll-1.39.pdf