## 4 Results and Analysis
### TextBlob Sentiment of the Arb-AppsReview Dataset  
As stated earlier, the original Arb-AppsReview dataset was labeled using the Camel tool. After applying the TextBlob tool for Arabic (TextBlob-ar 0.0.2 extension), the distribution of polarity of sentiment changed (see Figure 4). Based on the results presented in Tables 14-17, the following findings were observed, and can be summarized as follows:  
* All classifiers benefited more when the extracted features were concatenated. Among all the classifiers, SVM yielded the highest accuracy of 91.67%, with 0.913 precision, 0.927 recall, and 0.920 F1-score.
* Despite the feature extraction techniques used, all classifiers performed well when the TextBlob tool was used, which proves the efficacy of using TextBlob sentiments.
* In terms of F1-score, the SVM classifier outperformed all other ML models. It provided the highest F1-score of 0.920, followed by the bagging method.  
Figure 8: True-positive rate (TP Rate) and false-positive rate (FP Rate) of ML models with original sentiments using Word2 Vec.  
Figure 7: True-positive rate (TP Rate) and false-positive rate (FP Rate) of ML models with original sentiments using concatenated features.  
subsection presents the impact of concatenating several lexicon-based features with the TextBlob sentiment.  
3.2 Comparative Analysis of ML Models with TextBlob Sentiments Using Different Combinations of Lexicons  
It was noticed that the performance of ML classifiers was improved in terms of F1-score when the lexicons were used as a feature extraction technique. This section investigates the performance of the ML models, combining several lexicons on the TextBlob sentiment. First, we have investigated the impact of lexicons without concatenating the extracted features by BoW, TF-IDF, Word2 Vec, and the concatenated features. In the next section, the feature extraction techniques are combined with a lexicon-based model and formulate one dataset. As shown in Tables 21-24, the following findings are noted:  
* RF and SVM improved in terms of accuracy, with 70.08% and 71.26%, respectively.
* Using Bing Lui + MPQA lexicons led to degrading the performance of NB.
* A combination of the Bing Lui lexicon and the AFINN lexicon, in most cases, yielded the best results.
* The combination of all extracted features does not always improve the performance of the ML classifiers. This means that the researchers must carefully select the lexicon and investigate the performance of each classifier using all the possible combinations.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Classifier** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline RF & 68.26\% & 0.612 & 0.683 & 0.628 \\ Bagging & 71.86\% & 0.764 & 0.719 & 0.758 \\ SVM & 68.86\% & 0.616 & 0.689 & 0.631 \\ LR & 67.67\% & 0.620 & 0.677 & 0.636 \\ NB & 65.27\% & 0.615 & 0.653 & 0.619 \\ \hline \hline \end{tabular}
\end{table}
Table 19: Experiments with ML models and the AFFIN lexicon on the TextBlob sentiments.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Classifier** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline RF & 67.67\% & 0.599 & 0.677 & 0.628 \\ Bagging & 69.46\% & 0.711 & 0.695 & 0.736 \\ SVM & 67.67\% & 0.596 & 0.677 & 0.624 \\ LR & 68.86\% & 0.695 & 0.689 & 0.731 \\ NB & 68.86\% & 0.648 & 0.689 & 0.651 \\ \hline \hline \end{tabular}
\end{table}
Table 20: Experiments with ML models and the MPQA lexicon on the TextBlob sentiments.  
\begin{table}
\begin{tabular}{l c c c} \hline \hline
**Classifier** & **Accuracy** & **Precision** & **Recall** & **F1-Score** \\ \hline RF & 70.06\% & 0.638 & 0.701 & 0.650 \\ Bagging & 71.86\% & 0.764 & 0.719 & 0.758 \\ SVM & 68.86\% & 0.616 & 0.689 & 0.631 \\ LR & 67.67\% & 0.620 & 0.677 & 0.636 \\ NB & 65.27\% & 0.615 & 0.653 & 0.619 \\ \hline \hline \end{tabular}
\end{table}
Table 18: Experiments with ML models and the Bing Lui lexicon on the TextBlob sentiments.