## 2 Literature Review  
Several studies have looked at the issue of automatic TC, providing various methodologies and answers. This is primarily true for English, but it also applies to other languages, such as Arabic, it is still in an early stage [1]. The authors of [34] used a variety of categorization techniques to investigate the impact of removing stop words on ATC. They discovered that a support vector machine (SVM) classifier with little sequential optimization had the best error rate and accuracy. In [35], the Naive Bayes (NB) classifier is used to examine the impact of a light stemmer, Khoja stemmer, and root extractor on Arabic document categorization. The authors came to the conclusion that a root extractor combined with a position tagger would yield the most outstanding results. Chi-square (Chi2), Information Gain (IG), NG-Goh-Low (NGL), and Galavotti-Sebastiani-Simi Coefficient (GSS) coefficients were used to determine the essential features and the influence of feature reduction approaches on Arabic document categorization [22]. They also employed feature weighting approaches based on inverse document frequency (IDF), such as Term frequency (TF-IDF), the location of a word's first occurrence (FAiDF), and the compactness of the word (WCF) (CPiDF). The classification model was established using the SVM classifier. Thus, when the TF-IDF, CPiDF, and FAiDF feature weighting methods were combined, the GSS outperformed the other feature selection strategies. The feature selection approach for ATC by [36] was binary practical swarm optimization with a KNN classifier (BPSO-KNN). The Alj-News dataset was utilized to develop the classification model, and the best results were obtained utilizing SVM and NB classifiers. On Arabic document classification, [PERSON] et al. [37] tested a number of feature selection approaches, including Chi2, IG, Correlation (Corr), and SVM-based Feature Ranking Method (SVM-FRM). The classification model was constructed using an SVM classifier. In their research, they used the BBC [38] and Abuaiadah [39] databases. They arrived at the conclusion that SVM-FRM performs well in a balanced dataset, but not so well in an imbalanced dataset. A novel feature selection method, namely, improved chi-square (ImpCHI), was presented by [40] to enhance the ATC. Three standard features selection methods, namely, IG, Chi2, and Mutual Information (MI), were compared with ImpCHI. SVM and DT classifiers were used to evaluate the performance using CNN dataset [38]. Experimental results demonstrate that the most beneficial result was obtained using the ImpCHI FS method using the SVM classifier. In [41], the authors presented the Frequency Ratio Accumulation Method, a revolutionary text categorization technique for the Arabic language (FRAM). The characteristics were extracted using a bag of words (BoW). Chi2, MI, Odds Ratio (OR), and GSS-coefficient were among the feature selection approaches used to exclude unnecessary characteristics (GSS). According to the results, the FRAM outperformed the NB, Multi-variant Bernoulli Naive Bayes (MBNB), and Multinomial Naive Bayes models (MNB) classifiers. The macro-f-measure value was 95.1 percent for the unigram word-level representation approach. The Polynomial Networks (PNs) classifier used by [42] on Arabic text classification using the Alj-News dataset. They compared the performance of the PNs classifier with other classifiers, such as SVM, NB, and DT classifiers. Their results showed that the performance of the PNs classifier was not the best for all the categories in the dataset, but was very competitive. The authors in [43] claimed to be the first to utilize Logistic Regression (LR) in Arabic Text categorization with the Alj-News dataset. The results of the experiments showed that Logistic Regression is beneficial for ATC. The effects of eight supervising learning algorithms on Arabic document classification Studied by [44]. Several feature representation approaches were used to extract features from the Abuaiadah dataset [39].