Arabic Sentiment Analysis (ASA): A Comprehensive Survey of Approaches, Challenges, and Future Directions
TL;DR
This survey introduced a comprehensive systematic review of ASA research from 2018 to 2025, analyzing over 70 peer-reviewed studies and proposing future directions including cross-lingual transfer learning, multimodal sentiment analysis, domain-specific ASA applications.
Abstract
Arabic sentiment analysis (ASA) has received increasing attention due to the rich availability of Arabic content across online platforms. However, the Arabic language presents several issues including rich morphology, dialectal diversity, and complex grammatical structures. This survey introduced a comprehensive systematic review of ASA research from 2018 to 2025, analyzing over 70 peer-reviewed studies. We examine three main methodological approaches:(1) traditional machine learning approaches, (2) deep learning techniques, and (3) transformer-based architectures including AraBERT, MARBERT, and CAMeLBERT and emerging aspect-based sentiment analysis (ABSA) research. Additionally, we provide a novel comparative analysis of a set of publicly available datasets and NLP tools. We identify four critical challenges: data scarcity, code-switching, dialectal generalization and evaluation standardization. Finally, we propose future directions including cross-lingual transfer learning, multimodal sentiment analysis, domain-specific ASA applications. The survey provides researchers with a structured roadmap for improving Arabic sentiment analysis systems.