###### Abstract
A number of sentiment analysis studies have showed that convolution neural networks (CNN) and long-short-term memory networks (LSTM) [6] outperform the traditional machine learning approaches to detect sentiment. In addition, combining LSTM with CNN [7] showed promising results and surpassed the performance of the conventional machine learning models. Several research works were conducted for Arabic sentiment analysis (ASA) that gained more interest recently, especially during the COVID-19 pandemic. According to [8], there are three main approaches used for Arabic sentiment analysis, which are supervised, unsupervised, and hybrid methods. The research conducted obtained interesting outcomes, but at the same time, the results were more divergent because of the different types of methods used and Latin languages, where only a few research works were conducted for studying ASA, which focused on analyzing Arabic tweets. Therefore, more efforts are still needed to address the sentiment analysis of users' reviews on mobile applications, especially the applications that provide governmental services in health, education, and other sectors. Recently, different mobile applications were developed in Saudi Arabia after the COVID-19 pandemic. According to the World Health Organization (WHO), Saudi Arabia actively participated in fighting COVID-19 nationally, regionally, and globally. Locally, the government has taken several urgent actions to fight COVID-19 in different sectors, such as health, education, security, Islamic affairs, and others. Several mobile applications were developed to provide online services. For instance, these healthcare applications, namely Tawakkalna, Tetaman, Tabaud, Sehhaty, Mawid, and Sehhah, were successfully launched and used by millions of users in Saudi Arabia during the COVID-19 pandemic. This study will review the most recent studies in this field and propose a sentiment classification approach for measuring user satisfaction toward governmental services' mobile applications. This analysis will support the government officers to make better decisions regarding the improvements in the quality of the online services offered to citizens and residents. In addition, the paper will help the developers to improve any potential bugs or difficulties in these applications based on the users' opinions and experiences. The key contributions of this research paper can be summarized as follows:* This study presents the first use of the Arb-AppsReview dataset that is designed to capture users' reviews on the Google Play Store. The original dataset is available publicly on github.com at ([[https://github.com/Arb-AppsReview/Arb-AppsReview/blob/main/apps_ar_reviews_dataset.csv](https://github.com/Arb-AppsReview/Arb-AppsReview/blob/main/apps_ar_reviews_dataset.csv)]([https://github.com/Arb-AppsReview/Arb-AppsReview/blob/main/apps_ar_reviews_dataset.csv](https://github.com/Arb-AppsReview/Arb-AppsReview/blob/main/apps_ar_reviews_dataset.csv))) (accessed on 20 November 2021). The dataset comprises about 51,000 Arabic reviews related to six Saudi governmental services' apps. * The original dataset finds the sentiment score for each review by running the Camel tool ([[https://camel-tools.readthedocs.io/en/latest/api/sentiment.html](https://camel-tools.readthedocs.io/en/latest/api/sentiment.html)]([https://camel-tools.readthedocs.io/en/latest/api/sentiment.html](https://camel-tools.readthedocs.io/en/latest/api/sentiment.html))) (accessed on 1 March 2022). However, the authors found that some reviews were labeled incorrectly. Thus, the dataset was enriched by several lexical dictionaries. In this study, the Arabic TextBlob lexical dictionary was integrated firstly for annotating the users' reviews, and later the performance of the ML classifiers was compared with the original dataset labeled by the Camel tool.