## 2 Related Works  
Several studies have faced the challenge of classifying texts in different domains and languages, with a particular focus on English texts and tweets. The texts were classified in several phases, each with its own advantages and limitations. However, research on other languages such as Turkish, Portuguese, Arabic, and dialect texts remains relatively rare because these lack standardized spelling and extensive annotated corpora. A large number of research studies deal with low-resource languages in the field of text classification. Paper Paper [6] presented a study based on sentiment detection and text classification in Turkish. Most of the research dealing with text classification focuses on more commonly used languages, such as English and French, but neglects languages like Turkish. This study evaluated the performance of ChatGPT-3.5 and ChatGPT-4 in sentiment detection and text classification, using a dataset composed of comments collected on YouTube, with manually tagged news tweets. The authors in [7] treated the Greek language as a low-resource language. They evaluated a classification dataset created based on Greek social networks. This analysis compared machine learning models with a text classification model based on GREEK-BERT. To encourage further research in this area, the authors shared the source code of the best-performing model. Article [8] treated the Serbian language as a low-resource language. This language is distinguished by a limited number of corpora and processing tools. The authors of this study created a dataset based on poems written in Serbian. The aim was to analyze and evaluate the classification of the text, ranging from linear models to large language models. The authors in [9] stated that their aim was to diversify the sources of the data collected in order to fight the lack of datasets in Portuguese. This research compared different models of text classifiers. It covered both classical methods and current technologies in order to demonstrate the capabilities of the models and the challenges faced. All the research mentioned above focused on foreign languages with limited resources. In spite of its morphological and syntactic richness, Arabic is also considered an under-researched language. The following paragraphs in this section review research on text classification in Arabic. The study in [10] focused on classifying Arabic tweets into five categories: News, Conversation, Questions, Wishes, and Other. Two text representation methods were explored: (1) extracted text with TF-IDF and (2) word embeddings (Word2 Vec). Three classifiers--SVM, Gaussian Naive Bayes (GNB), and Random Forest (RF)--were evaluated. The findings revealed that traditional machine learning models, particularly SVM with an RBF kernel and RF, achieved the highest performance, with macro-F1 scores ranging from 98.09% to 98.14% when combined with stemming and TF-IDF. This exceeded the previous best score of 92.95% obtained by an RNN-GRU deep learning model. Interestingly, the use of word embeddings negatively impacted the performance of all classifiers, particularly GNB. Similarly, the authors in [11] investigated automatic text categorization for Arabic news articles, addressing both single-label and multi-label classification tasks. Two datasets were constructed: a single-label dataset comprising 90,000 articles across four domains (business, Middle East, technology, and sports) and a multi-label dataset containing over 290,000 articles. For single-label classification, ten surface-learning classifiers were evaluated, with SVM achieving the highest accuracy (97.9%). For multi-label classification, deep learning models--particularly CGRU--demonstrated superior performance, achieving an accuracy of 94.85%, while LSTM yielded the lowest accuracy (90.17%). These findings underscore the effectiveness of deep learning for multi-label text categorization in Arabic. The authors in [12] proposed a BERT model transformer to examine the impact of data diversity on topic classification in Arabic.