Artificial Intelligence for Text Analysis in the Arabic and Related Middle Eastern Languages: Progress, Trends, and Future Recommendations

Abstract

In the last 10 years, there has been a rise in the number of Arabic texts, which necessitates a more profound understanding of algorithms to efficiently understand and classify Arabic texts in many applications, like sentiment analysis. This paper presents a comprehensive review of recent developments in Arabic text classification (ATC) and Arabic text representation (ATR). We analyze the effectiveness of various models and techniques. Our review finds that while deep learning models, particularly transformer-based architectures, are increasingly effective for ATC, challenges such as dialectal variations and insufficient labeled datasets remain key obstacles. However, developing suitable representation models and designing classification algorithms is still challenging for researchers, especially in Arabic. A basic introduction to ATC is provided in this survey, including preprocessing, representation, dimensionality reduction (DR), and classification with many evaluation metrics. In addition, the survey includes a qualitative and quantitative study of the ATC’s existing works. Finally, we conclude this work by exploring the limitations of the existing methods. We also mention the open challenges related to ATC, which help researchers identify new directions and challenges for ATC.