## 5 Conclusions  
This paper presented the Arabic sentiment analysis on the reviews of six governmental mobile applications from Google Play and the App Store. Several ML models were applied  
Figure 10: Percentage of change of the ML classifiers with respect to the balancing techniques. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Classifier** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline RF & 93.87\% & 0.935 & 0.936 & 0.935 \\ Bagging & 91.89\% & 0.919 & 0.919 & 0.919 \\ SVM & 94.38\% & 0.943 & 0.943 & 0.943 \\ LR & 92.68\% & 0.926 & 0.928 & 0.927 \\ NB & 93.11\% & 0.930 & 0.931 & 0.930 \\ \hline \hline \end{tabular}
\end{table} 
Table 31: Performance results of ML models using the SMOTE dataset and the concatenated features and all lexicons. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Classifier** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline The proposed method (SVM) & 94.38\% & 0.943 & 0.943 & 0.943 \\ The previous study [49] (KNN) & 78.46\% & 0.799 & 0.780 & 0.790 \\ \hline \hline \end{tabular}
\end{table} 
Table 32: Comparison results of the proposed ML models with a previous study. to the AppsReview dataset, which includes 51k reviews. These ML methods were: RF, bagging, SVM, LR, and NB. In the conducted experiments, we evaluated the ML performance by integrating different feature extraction techniques, which were Bing Lui, AFINN, and MPQA lexicons. In addition, the performance of the ML models was investigated using an imbalanced and a balanced dataset. As balancing techniques, both under-sampling and oversampling were used. In this regard, the SMOTE technique was applied as an oversampling technique. The experimental results showed that when the features were extracted by BoW using the sentiments of the original Arb-AppsReview dataset, the NB classifier performed better than the other models (74.25% for accuracy). Then, when the TextBlob sentiment tool was applied to the Arb-AppsReview dataset, it was found that all classifiers performed better when the extracted features were concatenated. Among all the classifiers, SVM yielded the highest accuracy of 91.67%. Then, the findings showed that the highest accuracy score of 93.17% was achieved by SVM when it was integrated with the concatenated features and the features extracted by the proposed lexicons. Finally, the outcomes of the ML models using the SMOTE technique, the concatenated features, and all lexicons obtained the best results. For instance, SVM with these preprocessing methods obtained an accuracy of 94.38%, which overcame all other models. This study recommends applying the used feature engineering methods with the SMOTE oversampling technique to obtain better ASA results. Since the proposed model relies on the translation of reviews using google translation, the quality of the model might be affected by the quality of the translation. For future work, it is recommended to investigate the machine translation techniques to obtain high-quality translations that might increase the model performance. Additionally, it is recommended to explore the effect of applying different feature extraction and selection methods to the dataset used in this research and conduct more experiments that apply different combinations of deep learning methods on the Arb-AppsReview dataset to enhance the Arabic sentiment analysis. Conceptualization, M.A.-S., F.S. and M.H. ; methodology, M.A.-S., F.S. and M.A.A.-H.; software, M.A.-S.; validation, M.H. and M.A.A.-H.; formal analysis, M.A.-S., F.S. and M.H. ; investigation, M.H. and M.A.-H.; resources, M.H., M.A.-S. and M.A.A.-H.; data curation, M.A.-S.; writing--original draft preparation, M.A.-S., F.S. and M.H. ; writing--review and editing, M.A.-S., F.S. and M.H. ; visualization, M.A.-S.; supervision, M.A.-S. and F.S. ; project administration, M.H. and M.A.A.-H.; funding acquisition, M.H. and M.A.A.-H. All authors have read and agreed to the published version of the manuscript.