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  • Publication
    Comparative Study on Arabic Text Classification: Challenges and Opportunities
    (2023) ;
    Abualigah, Laith
    Oliva, Diego
    Hussien, Abdelazim G.
    Melhem, Mohammed K. Bani
    There have been great improvements in web technology over the past years which heavily loaded the Internet with various digital contents of different fields. This made finding certain text classification algorithms that fit a specific language or a set of languages a difficult task for researchers. Text Classification or categorization is the practice of allocating a given text document to one or more predefined labels or categories, it aims to obtain valuable information from unstructured text documents. This paper presents a comparative study based on a list of chosen published papers that focus on improving Arabic text classifications, to highlight the given models and the used classifiers besides discussing the faced challenges in these types of researches, then this paper proposes the expected research opportunities in the field of text classification research. Based on the reviewed researches, SVM and Naive Bayes were the most widely used classifiers for Arabic text classification, while more effort is needed to develop and to implement flexible Arabic text classification methods and classifiers.