## 2 Related Work
### Sentiment Analysis  
Sentiment analysis aims to detect and extract public sentiment and viewpoints from text found in reviews, blogs, social media, news, forums, and other online sources. Therefore, businesses and brands employ sentiment analysis to comprehend the social sentiment surrounding their product, service, or brand on social media or throughout the Internet. Generally, it is regarded as a machine learning task that examines texts for polarity, such as \"negative\", \"positive\", or \"neutral\", as well as more severe emotions, such as happiness, sadness, anger, interest, etc. [23]. Several researchers have used this method to assess user experience using reviews. [PERSON] et al. [24] offered a framework for a recommendation system that the government could use to promote their Gov2 Go super application in the Indonesian community, which replaced thousands of applications to support electronic governance. This methodology employed sentiment analysis based on expectation confirmation theory (ECT), which examines user pleasure, to provide suggestions that take advantage of the user's past contact with the product or service. They used long short-term memory (LSTM) and naive Bayes (NB), resulting in accuracy rates of 97.49% and 94.22%, respectively. [PERSON] et al. [25] conducted another study on super applications using sentiment analysis. They focused on the Fintech sector, which relates to financial technology that utilizes smartphones, software,and modern technology to provide financial services and handle or transfer money in a simpler, easier, and smarter way. The authors used sentiment analysis at the sentence level on collected reviews for the OVO app in Tegal City in Indonesia to determine whether it is a good form of Fintech or not. They applied support vector machine (SVM) with particle swarm optimization (PSO), achieving an accuracy of 82.33%. While the preceding studies utilized other languages, [PERSON] et al. (2018) employed the Arabic language and assessed user opinion of eight applications for mobile banking in Yemen using sentiment analyses. Using NB, SVM, K-nearest neighbor (KNN), and decision tree (DT), accuracy rates of 89.65%, 68.85%, 87.89%, and 56.02%, respectively, were obtained. In the same context, [PERSON] et al. (2018) reviewed six government applications--Tetaman, Tawakkalna, Sehhaty, Sehhah, Tabaud, and Mawid--that were introduced by the Saudi government during the COVID-19 pandemic to evaluate and assess user opinions and satisfaction using a sentiment analysis approach. They used SVM, DT, NB, and KNN, which achieved the best accuracy of 78.46% for the KNN classifier. By optimizing the size of the dataset and utilizing several machine learning approaches, they were able to improve their work and achieve good results ([PERSON] et al., 2018). They balanced the data via the synthetic minority oversampling technique (SMOTE) and employed logistic regression (LR), random forest (RF), SVM, and bagging for classification. They attained the highest accuracy using the SVM method, achieving an accuracy of 94.38%. Likewise, [PERSON] et al. (2018) used sentiment analysis to analyze online government services using two approaches, namely, a lexicon-based and manual annotation approach, to assess customer satisfaction with the Saudi Ministry of Commerce (MOC)'s commercial e-services. They gathered reviews from the \"Commercial Report\" app in the Apple Store, as well as Twitter mentions of customer support and official MOC accounts. Then, they cleaned the collected data. To apply the first technique, the researchers manually annotated the data as positive, neutral, or negative, yielding 1.9% positive, 91.2% neutral, and 6.8% negative. During the annotation process, they collected 140 negative and 140 positive terms to be used with the lexicon-based technique. They then tested these two techniques using three classifiers, NB, SVM, and LR, which resulted in the annotation method outperforming the lexicon-based method. The SVM classifier achieved the best accuracy of 94%.