###### Abstract  
Analyzing the sentiment of Arabic texts is still a big research challenge due to the special characteristics and complexity of the Arabic language. Few studies have been conducted on Arabic sentiment analysis (ASA) compared to English or other Latin languages. In addition, most of the existing studies on ASA analyzed datasets collected from Twitter. However, little attention was given to the huge amounts of reviews for governmental or commercial mobile applications on Google Play or the App Store. For instance, the government of Saudi Arabia developed several mobile applications in healthcare, education, and other sectors as a response to the COVID-19 pandemic. To address this gap, this paper aims to analyze the users' opinions of six applications in the healthcare sector. An improved sentiment classification approach was proposed for measuring user satisfaction toward governmental services' mobile apps using machine learning models with different preprocessing methods. The Arb-AppsReview dataset was collected from the reviews of these six mobile applications available on Google Play and the App Store, which includes 51k reviews. Then, several feature engineering approaches were applied, which include Bing Liu lexicon, AFINN, and MPQA Subjectivity Lexicon, bag of words (BoW), term frequency-inverse document frequency (TF-IDF), and the Google pre-trained Word2 Vec. Additionally, the SMOTE technique was applied as a balancing technique on this dataset. Then, five ML models were applied to classify the sentiment opinions. The experimental results showed that the highest accuracy score (94.38%) was obtained by applying a support vector machine (SVM) using the SMOTE technique with all concatenated features. sentiment analysis; machine learning; classification; ensemble learning; feature selection iacr 202220220220220220220220220220220220220220220220220220220220220220222022022202202202202202202202220220222022022202202220222022202202220222022202220222022  
these applications. Due to the huge expansion of the content posted on these sites daily, institutions including government agents and private companies have exploited people's expressions and opinions regarding the services or products that are provided online. Sentiment analysis is the task of utilizing text mining, natural language processing (NLP), and computational linguistics approaches to systematically detect, analyze, and examine the users' opinions appearing in subjective textual information. Sentiment analysis processes concentrate on detecting text containing opinions and deciding whether these opinions are positive, negative, or neutral comments [1; 2]. Recently, sentiment analysis approaches have relied on inspecting opinions or emotions of diverse subjects, such as peoples' opinions and impressions about movies, products, and daily affairs. In the literature, several researchers have utilized supervised machine learning algorithms, especially classification methods, for sentiment analysis purposes, such as support vector machine (SVM) and naive Bayes classifiers. [PERSON] [3] showed that the SVM classifier with both TF-IDF and stemming outperformed the naive Bayes classifier for Arabic tweets' sentiment analysis. Similarly, [PERSON] et al. [4] combined machine learning classifiers, ISRI Arabic stemmer, and the cosine similarity to propose a better-performing hybrid method. The experimental results in [4] demonstrated that the ISRI Arabic stemmer with linear SVM can achieve higher accuracy compared to other stemmers, such as the Porter stemmer. In addition, [PERSON] and [PERSON] [5] used SVM and naive Bayes classification algorithms for sentiment analysis. Unigram and bigram features were extracted to train the classifiers. The outcomes showed that SVM performed better than NB on the collected tweets using Twitter API. On the other hand, deep neural networks (DNN) have been applied and shown a good performance compared to traditional machine learning in terms of detecting sentiments of short texts.