## 2 Related Works
They compared BERT models pre-trained on formal text with those pre-trained on a combination of formal and informal Arabic data. Their findings indicate that expanding the training data--either by incorporating diverse data during model pre-training or by using varied datasets for specific topic classification--consistently enhances classification performance. The study in [13] addressed the complex task of classifying the Arab patient experience (PX). The authors developed models to categorize patient comments into 25 distinct classes. To overcome the limitations of manual annotation, they explored deep learning and BERT-based models. They then evaluated various architectures, including BiLSTM, BiGRU, several pre-trained Arabic BERT models, and a newly developed domain-specific BERT model (PX-BERT) fine-tuned on the PX dataset. The results showed that AraBERTv02 achieved the best performance on a dataset of 19,000 comments, while PX-BERT outperformed other models when tested on a subset of 13,000 exclusively negative comments. These findings stress the potential of domain-specific BERT models, highlighting the effectiveness of high-quality, pre-trained domain-specific models for classifying Arabic PX comments. Other studies examined sentiment analysis as a sub-branch of text classification, for Arabic and Arabic dialect. For instance, the authors in [14] explored a deep learning approach to Arabic sentiment analysis based on three architectures: DNN, Deep Belief Networks (DBN), and Deep Autoencoders. The experiments were conducted on the Linguistic Data Consortium Arabic Treebank (LDC ATB) dataset, using sentiment scores from the ArSenL lexicon as feature vectors. The deep autoencoders provided a more accurate representation of the sparse input data. Additionally, the study introduced a fourth model, the Recursive Autoencoder (RAE), which achieved the best performance without relying on a sentiment lexicon. In [15], the authors proposed a new model for classifying tweets into three categories: positive, negative, and neutral. The dataset consisted of pre-processed tweets in Jordanian dialectA Arabic. A comprehensive evaluation was performed using supervised machine learning techniques. The results demonstrated that an SVM classifier with light stemming on Arabic text outperformed a Naive Bayes (NB) classifier. Furthermore, incorporating a correlation analysis between the three sentiment categories and reducing the training set to include only the most frequent instances significantly improved the model's accuracy, yielding an SVM accuracy of 82.1%. Overall, traditional ML seem to dominate classification of small datasets [10; 15] whereas BERT models show promise when data is heavy or domain-tailored [12; 13]. This paper aims to fill important research gaps on the classification of texts in the Moroccan dialect. While previous work has explored various techniques for classifying Arabic texts in other dialects and in formal standard Arabic, the specific challenges of the Moroccan dialect continue to be under-investigated. Existing studies typically utilize significantly larger datasets. To address the limited labelled data available for Moroccan dialect, we worked with 10,200 rows after employing the AugGPT approach to augment our initial dataset of 3000 rows. This study examined a range of algorithms, including traditional machine learning, deep learning, and large language models, for multi-class classification. The best performing model will later be integrated into a chatbot for public use.