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

2025-02-06

AI Summary

Arabic sentiment analysis (SA) lags significantly behind English SA due to systemic challenges—including severe data scarcity, extreme dialectal diversity, and poor model generalizability. This paper presents the first comprehensive survey and empirical evaluation of Arabic SA, focusing on deep learning approaches (e.g., LSTM, BERT variants), cross-lingual transfer, data augmentation, and multi-dialect modeling. We systematically identify and structure six core challenges: annotation inconsistency, insufficient dialect coverage, resource bias, morphological complexity, domain mismatch, and evaluation fragmentation. Further, we propose four actionable research directions: (1) development of unified, multi-granularity benchmark datasets; (2) lightweight dialect-adaptation frameworks; (3) robust cross-dialect pretraining strategies; and (4) standardized evaluation protocols. Our work delivers the field’s first authoritative research gap map and practical roadmap, substantially enhancing comparability, reproducibility, and methodological rigor in future Arabic SA research.

Technology Category

• Natural Language Processing: Sentiment Analysis, Stylistic Analysis, and Argument Mining
• Machine Learning: Large Multimodal Models (LMMs)
• Knowledge Representation and Reasoning: Argumentation

Application Category

• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• Web Mining and Content Analysis: Mining multimedia, multimodal, multilingual, cross-lingual Web data
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

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.

Problem

• Survey of Arabic sentiment analysis methods
• Identify challenges in Arabic sentiment research
• Propose future directions for Arabic analysis

Innovation

• Arabic sentiment analysis survey
• Deep learning methods focus
• Identifies research gaps challenges