## 4 Results and Analysis
### Arab TextBlob Sentiment of the Arb-AppsReview Dataset with Enriched Features Extracted by Lexicons  
Tables 25-28 show that the highest accuracy score of 93.17% was achieved by SVM, with the enriched features extracted by lexicons. The results confirm the efficiency of SVM when it is integrated with the concatenated features and the features extracted by the proposed lexicons. In addition, the results show that using TextBlob for revising the sentiment of the original dataset is an efficient tool that leads to improving the performance of the ML models. It is also obvious that the performance of ML models is affected differently by the word representation approaches, e.g., RF yielded an accuracy score of 86.14% when the Word2 Vec technique was used, whilst it yielded accuracy scores of 85.44% and 84.44% when BoW and TF-IDF were used, respectively. Similarly, the bagging approach yielded different accuracy scores where the BoW technique afforded the highest score. Figure 9 shows a comparison between the results presented in Table 17 and the results obtained by concatenating all features and lexicons with TextBlob sentiment. The results show that the performance of the ML classifiers benefited more when TextBlob sentiment and the features were extracted by all lexicons and word representation techniques. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Classifier** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline RF & 65.87\% & 0.588 & 0.659 & 0.612 \\ Bagging & 70.06\% & 0.623 & 0.701 & 0.639 \\ SVM & 68.86\% & 0.601 & 0.689 & 0.631 \\ LR & 68.26\% & 0.593 & 0.683 & 0.631 \\ NB & 68.86\% & 0.643 & 0.689 & 0.656 \\ \hline \hline \end{tabular}
\end{table} 
Table 23: Experiments with ML models and AFINN + MPQA lexicons on the TextBlob sentiments. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Classifier** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline RF & 68.26\% & 0.629 & 0.683 & 0.646 \\ Bagging & 71.86\% & 0.751 & 0.719 & 0.712 \\ SVM & 71.26\% & 0.747 & 0.713 & 0.572 \\ LR & 64.67\% & 0.563 & 0.647 & 0.602 \\ NB & 59.88\% & 0.600 & 0.599 & 0.575 \\ \hline \hline \end{tabular}
\end{table} 
Table 22: Experiments with ML models and Bing Lui + MPQA lexicons on the TextBlob sentiments. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Classifier** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline RF & 68.46\% & 0.647 & 0.695 & 0.654 \\ Bagging & 71.26\% & 0.632 & 0.713 & 0.649 \\ SVM & 69.46\% & 0.636 & 0.695 & 0.638 \\ LR & 65.87\% & 0.579 & 0.659 & 0.613 \\ NB & 64.67\% & 0.617 & 0.647 & 0.620 \\ \hline \hline \end{tabular}
\end{table} 
Table 24: Experiments with ML models and all lexicons on the TextBlob sentiments. \begin{table}
\begin{tabular}{l l l l l} \hline \hline
**Classifier** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline RF & 68.26\% & 0.629 & 0.683 & 0.646 \\ Bagging & 71.86\% & 0.751 & 0.719 & 0.712 \\ SVM & 71.26\% & 0.747 & 0.713 & 0.572 \\ LR & 64.67\% & 0.563 & 0.647 & 0.602 \\ NB & 59.88\% & 0.600 & 0.599 & 0.575 \\ \hline \hline \end{tabular}
\end{table} 
Table 22: Experiments with ML models and Bing Lui + MPQA lexicons on the TextBlob sentiments. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Classifier** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline RF & 84.25\% & 0.887 & 0.843 & 0.812 \\ Bagging & 80.06\% & 0.861 & 0.725 & 0.786 \\ SVM & 76.47\% & 0.818 & 0.765 & 0.752 \\ LR & 83.13\% & 0.857 & 0.831 & 0.827 \\ NB & 84.25\% & 0.832 & 0.843 & 0.832 \\ \hline \hline \end{tabular}
\end{table} 
Table 26: Experiments with ML models and TF-IDF and all lexicons on Arab TextBlob sentiments. \begin{table}
\begin{tabular}{l c c c} \hline \hline
**Classifier** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline RF & 86.14\% & 0.848 & 0.860 & 0.823 \\ Bagging & 84.25\% & 0.788 & 0.843 & 0.799 \\ SVM & 80.66\% & 0.744 & 0.807 & 0.752 \\ LR & 86.10\% & 0.880 & 0.860 & 0.867 \\ NB & 78.86\% & 0.763 & 0.789 & 0.770 \\ \hline \hline \end{tabular}
\end{table}