Abstract: Arabic is a complex language for text analysis because of its orthographic features, rich synonyms, and semantic style. Thus, Arabic text must be prepared more carefully in the preprocessing stage for the analyzer to improve the quality of the results. Moreover, many preprocessing steps have been proposed to improve the text analyzer quality by reducing high dimensionality, selecting the proper features to describe the text, and enhancing the process speed. This paper deeply investigates and summarizes the use of Arabic preprocessing techniques in Arabic text in general and focuses in-depth on clustering. Moreover, it focuses on seven preprocesses that are now used to prepare Arabic and provides the available tools for each of them; the seven preprocess are tokenization, normalization, stopword removal, stemming, vectorization, lemmatization, and feature selection. In addition, this paper investigates any work that uses synonyms and semantic techniques for preprocessing to prepare the text or reduce the dimensionality of the clustering algorithm. Therefore, this survey investigated nine techniques for Arabic text preprocessing to identify the challenges in this area. Finally, this study aims to serve as a reference for researchers interested in this area, and ends with potential future research directions.

Tahani Almutairi, Shireen Saifuddin, Reem Alotaibi, Shahendah Sarhan and Sarah Nassif, “Preprocessing Techniques for Clustering Arabic Text: Challenges and Future Directions” International Journal of Advanced Computer Science and Applications(IJACSA), 15(8), 2024. http://dx.doi.org/10.14569/IJACSA.2024.01508126

@article{Almutairi2024,
title = {Preprocessing Techniques for Clustering Arabic Text: Challenges and Future Directions},
journal = {International Journal of Advanced Computer Science and Applications},
doi = {10.14569/IJACSA.2024.01508126},
url = {http://dx.doi.org/10.14569/IJACSA.2024.01508126},
year = {2024},
publisher = {The Science and Information Organization},
volume = {15},
number = {8},
author = {Tahani Almutairi and Shireen Saifuddin and Reem Alotaibi and Shahendah Sarhan and Sarah Nassif}
}