## 2 Related Works  
The Arabic language has special characteristics that add additional challenges when addressing the Arabic sentiment analysis. These challenges include morphological analysis, dialect Arabic, Arabizi, and named entity recognition, in addition to the Arabic structure complexity and having different cultures [8; 9]. This makes it necessary to conduct more research and propose new methods for enhancing the sentiment analysis in Arabic. More efforts were made to analyze data from Twitter for several purposes, such as analyzing the users' opinions about online learning during COVID-19. This section focuses on reviewing the existing studies that used machine leaching and deep learning for Arabic sentiment analysis. More focus will be given to the studies that were applied for addressing the sentiment analysis of the Saudi Arabia community. According to [10], there are many benefits to analyzing Arabic sentiment, such as showing valuable insights for different provided services [11], recognizing the potential influencers in social media [12], and email spam detection [13]. There are three main strategies with different challenges for ASA, and these include preprocessing, feature generation, and selection and classification methods. Several studies proposed different methods for each of these strategies to enhance the performance of ASA. For instance, applying different preprocessing methods such as stemming, tokenization, and normalization can improve the performance of the sentiment analysis [10]. In addition, the effect of stemming on the ASA problem was studied in [14]. They used the Arabic root stemmer of Khoja and the light stemmer on two datasets and found that the latter performed better for sentiment classification. Other studies highlighted the importance of data preprocessing for Arabic sentiment analysis. For instance, the authors of [15] recommended that the preprocessing tools such as normalization, tokenization, and stop words' removal should be utilized in all ASA studies to improve the performance of the analysis. Additionally, they recommended ending the preprocessing by applying stemming methods, although aggressive stemming was not recommended as it might change the Arabic words' polarity. It is worth mentioning that most of the research efforts on the Arabic language aimed to discuss the modern standard form. However, by browsing the social media websites, we can find that majority of the Arabic users used their dialects, which generates a huge amount of Arabic dialects texts [16]. Different Arabic dialects were addressed, such as Saudi, Iraqi, Egyptian, Jordanian, Palestinian, and Algerian dialects [16]. According to [17], most of the studies focused on Jordanian (38%), Egyptian (23%), and Saudi (15%) dialects. For instance, [PERSON] et al. [18] applied automatic extraction of opinions on social media that are written in modern standard Arabic and Egyptian dialects, and they automatically analyzed sentiment into either positive or negative. [PERSON] et al. [19] applied ML methods for analyzing the sentiment of Arabic dialects. They applied different classifiers using a labeled dataset and found that the classifiers obtained good results using different evaluation metrics. To address the challenges of the Arabic language, [PERSON] and [PERSON] [20] created both stemmed and lemmatized versions of word lexicons for integrating the morphological notion. Then, a supervised model was constructed from a set of features. In addition, they semantically segmented the lexicon for reducing the vector's size of the model and enhancing the execution time. In addition, [PERSON] et al. [21] proposed lexicon-based and ML methods for sentiment analysis. They used the Arabic tweets dataset. The outcomes showed that ML methods, especially logistic model trees and SVM, outperformed the lexicon-based methods in predicting the subjectivity of tweets. The outcomes also showed the importance of applying feature selection for improving the performance of ASA. Several studies were conducted on ASA in Saudi Arabia.