Advancing Arabic Sentiment Analysis: ArSen Benchmark and the Improved Fuzzy Deep Hybrid Network
Yang Fang, Cheng Xu, Shuhao Guan, Nan Yan, Yuke Mei · 2024
Sentiment analysis is crucial in Natural Language Processing as it enables the extraction of opinions and emotions from text.However, Arabic sentiment analysis is often overlooked.Current benchmarks for Arabic sentiment analysis tend to be outdated or lack comprehensive annotations, which limits the development of more accurate and reliable models for the Arabic language.To address these challenges, we introduce ArSen, a meticulously annotated Arabic dataset centered on COVID-19, along with IFDHN, a novel model that employs fuzzy logic for more precise sentiment classification 1 .ArSen offers a robust and contemporary benchmark, and IFDHN achieves state-ofthe-art performance in Arabic sentiment analysis, with 78.12% accuracy, an F1-Macro score of 55.83%, and an F1-Micro score of 78.12% on the test set.Notably, by using only 0.23% of the computational resources of large language models, IFDHN achieved performance comparable to LLaMA-3-8B, showcasing significant improvements over existing methods.