A comprehensive survey of contemporary Arabic sentiment analysis: Methods, Challenges, and Future Directions

TL;DR

This survey addresses the gap in Arabic sentiment analysis by systematically reviewing contemporary deep-learning methods and situating ASA within the broader SA landscape. It introduces a three-dimensional taxonomy (modality, granularity, application) and catalogs key datasets and models, highlighting dialectal variation, data scarcity, and computational challenges. The work identifies gaps in multimodal data, fine-grained tasks, and cross-dialect transfer, and outlines future directions including large language models, interpretable SA, and richer ABSA/MAST benchmarks. Overall, the paper provides a strategic roadmap for advancing Arabic SA and aligning it with general SA progress while accounting for linguistic and cultural idiosyncrasies.

Abstract

Sentiment Analysis, a popular subtask of Natural Language Processing, employs computational methods to extract sentiment, opinions, and other subjective aspects from linguistic data. Given its crucial role in understanding human sentiment, research in sentiment analysis has witnessed significant growth in the recent years. However, the majority of approaches are aimed at the English language, and research towards Arabic sentiment analysis remains relatively unexplored. This paper presents a comprehensive and contemporary survey of Arabic Sentiment Analysis, identifies the challenges and limitations of existing literature in this field and presents avenues for future research. We present a systematic review of Arabic sentiment analysis methods, focusing specifically on research utilizing deep learning. We then situate Arabic Sentiment Analysis within the broader context, highlighting research gaps in Arabic sentiment analysis as compared to general sentiment analysis. Finally, we outline the main challenges and promising future directions for research in Arabic sentiment analysis.