## 2 Related Works
For instance, [PERSON] et al. [22] developed a prediction model for people's awareness of the precautionary procedures in Saudi Arabia. They used an Arabic COVID-19-related dataset that was generated from Twitter. Three predictive models were applied, which included SVM, K-NN, and naive Bayes, with the N-gram feature extraction method. The experimental results showed that SVM with bigram in TF-IDF outperformed other methods, and obtained 85% of prediction accuracy. The applied method was recommended to help the decision-makers in the medical sectors to apply different procedures in each region in Saudi Arabia during the pandemic. In addition, the authors of [23] studied the attitude of individuals in Saudi Arabia about online learning. They collected Arabic tweets posted in 2020 and applied sentiment analysis to this dataset. The results showed that people have maintained a neutral response toward online learning. The authors of [24] also collected a dataset that includes 8144 tweets related to Qassim University in Saudi Arabia. They applied one-way analysis of variance (ANOVA) as a feature selection method for removing the irrelevant and redundant features for sentiment analysis of Arabic tweets. Then, the results showed that SVM and naive Bayes achieved the best results with one-way ANOVA compared to other ML methods on the same dataset. Deep learning was also applied to enhance the performance of Arabic sentiment analysis. Several studies utilized deep learning methods for this purpose; for instance, the authors of [25] used an ensemble model that combined convolutional neural network (SNN) and long-short-term memory (LSTM) methods for analyzing the sentiment of Arabic tweets. The proposed model in [25] achieved a 64.46% F1-score, which is considered higher than the applied deep learning methods on the same dataset. Moreover, the authors of [26] proposed a feature ensemble model of surface (manually extracted) and deep (sentiment-specific word embedding) features. The models were applied on three Arabic tweets datasets. The results showed that the proposed ensemble of surface and deep features models obtained the highest performance for sentiment analysis. In addition, [PERSON] [27] proposed a corpus of 40,000 labeled Arabic tweets and applied 3 deep learning models for Arabic sentiment analysis, which were CNN, LSTM, and recurrent convolution neural network (RCNN). The results showed that LSTM outperformed CNN and RCNN with an average accuracy of 81.31%. Additionally, it was found that when data augmentation was applied to the corpus, the accuracy of LSTM increased by 8.3%. Another study on ASA [28] applied the multilayer, bidirectional, long-short-term memory (BiLSTM) method, that used the pre-trained word-embedding vectors. The applied model showed a notable enhancement in the performance of sentiment analysis compared to other models. The authors of [29] extracted Twitter data from different cities in Saudi Arabia. NLP and ML methods were used to analyze the sentiments of individuals during the COVID-19 pandemic. This research collected Arabic tweets, and then after manual annotation to classify the tweets into different sentiments, such as negative, positive, neutral, etc., they applied LSTM and naive Bayes for classification. Similar to other studies, the results here showed that the LSTM model performed better than other models and obtained high accuracy. In [30], the performance of ML-based models, as well as the performance of deep learning models such as CNN, LSTM, CNN-LSTM, and Bi-LSTM, was examined using a set of 17,155 tweets about e-learning systems. The authors adopted TextBlob, VADER (Valence Aware Dictionary for Sentiment Reasoning), and SentiWordNet to analyze the polarity and subjectivity score of tweets' text. To ensure the quality of the dataset, the SMOTE technique was applied as a balancing technique for the dataset. The results showed that the TextBlob technique yielded the best accuracy of 94% when applied with the Bi-LSTM model.