Data Mining for Arabic Text Classification: Methods, Challenges, and Future Directions

Arabic is a universally dominant language, yet it simultaneously poses some fascinating computational challenges because of its complicated morphology, dialect variation, and script-dependent nature. Regardless of these challenges, the Arabic NLP research community has witnessed considerable advancement in recent years, driven by the rising demand for sentiment analysis, text classification, and other data-driven applications. This study highlights the considerable efforts of data mining approaches for Arabic language processing, particularly in text classification and sentiment analysis. It also discusses particular challenges of the language, i.e., script-based issues, intricate morphology, and varied dialects, which render natural language processing more challenging for Arabic. Besides, it identifies key research gaps that are currently present, offering valuable suggestions for guiding future developments in robust and advanced natural language processing systems with particular emphasis on Arabic.