## 2 Related Works
### Arabic Language  
In study [7], the authors applied ML models to classify Arabic YouTube comments, achieving the highest performance accuracy of 94.62% with Naive Bayes. The authors of [8] researched Saudi Arabian citizens' opinions using ML and sentiment analysis on a sizeable Arabic user review dataset. They found that the K-Nearest Neighbors (KNN) method achieved the highest accuracy, at 78.46%. The authors in [9] curated a dataset of 15,050 Arabic YouTube comments, focusing on detecting antisocial behavior in Arabic online interactions, and achieved a 90.05% success rate with Support Vector Machine (SVM) classification. [PERSON] et al. [10] developed a DL model combining LSTM, GRU, and ensemble methods for ASA. It converts text into vectors using pre-trained embeddings, extracts features with LSTM and GRU, and combines their outputs via voting for sentiment classification. Evaluated on six datasets, it achieved a top accuracy of 94.32%, outperforming existing benchmarks. [PERSON] et al. [11] introduced ArCAR, a DL model for character-level Arabic text recognition using a Convolutional Neural Network (CNN) with six layers and two fully connected layers. Tested on datasets, including a HARD binary classification (balanced) and a three-class classification (imbalanced), the model achieved accuracies of 93.58% and 93.23%, respectively. In another study [12], the LSTM algorithm was evaluated on an Arabic Tweet dataset from the University of California, Irvine Machine Learning Repository (UCI ML Repository). The LSTM classified tweets into positive and negative sentiments, achieving 89.8% accuracy, surpassing previous studies using traditional ML methods on the same dataset. [PERSON] et al. [13] used a DL approach to analyze sentiment in Arabic tweets, focusing on both Modern Standard Arabic and Egyptian dialects. They created an annotated corpus categorizing tweets into positive and negative sentiments. Among CNN, LSTM, and RCNN techniques, LSTM performed best with an accuracy of 81.31%. Moreover, [PERSON] et al. [14] introduced a novel deep-learning model that integrates a single layer of the CNN algorithm with two layers of the LSTM algorithm to analyze the sentiment of Arabic tweets. The model employs fastText (Skip-gram) word embedding as the input layer to transform words into vectors. To enhance the results, the model's output is further processed using the SVM technique. This proposed model showed impressive performance, achieving an accuracy of 90.75%. Study [15] examined Arabic dialectal sentiment analysis using a hybrid deep neural network combining bi-directional LSTM and CNN. Tested on Arabic Sentiment Tweets Dataset (ASTD), Shami-Senti, and LABR datasets, the model notably achieved 93.5% accuracy on Shami-Senti, outperforming baseline models. [PERSON] et al. [16] explored the challenges and methodologies in ASA, focusing on the unique morphological complexities and dialectal variations of the language. Their study employed DL algorithms, including CNNs, long short-term memory (LSTM), and a hybrid CNN-LSTM model. They utilized word embeddings like Word2 Vec and fastText to enhance model performance rather than relying on hand-crafted features. Evaluating their models on the Hotel Arabic Reviews Dataset (HARD) and the Large-Scale Arabic Book Reviews (LABR) dataset, they demonstrated that the fastText embedding, particularly with the HARD 2-imbalance dataset, achieved superior results. Their CNN model, in particular, achieved notable accuracy rates, outperforming other models and existing state-of-the-art results on the same datasets. Multiple recent studies [17; 18] have trained the BERT model using Arabic data from Wikipedia and the Oscar dataset. Additionally, other studies [19; 20] have fine-tuned the Arabic BERT model from reference [17] specifically for sentiment analysis. Researchers [21]achieved an impressive accuracy of 95.7% in ASA using hULMonA and multilingual BERT, highlighting their effectiveness in NLP tasks. [PERSON] et al.