## 6 Experimental Results
### Feature Importance Comparison Analysis  
Based on the previous experimental results, the EFF-SA algorithm is based on a forward chain of processes for performing a feature selection on the split features. As mentioned before, the sentiment analysis documents and sentiments are divided into \(k\) features for training and testing. The correlation methods are applied with different ML models for measuring and scoring each set of \(k\) features. On the subsequent stages, the features are accumulated with additional \(k\) features, while the ML models perform the scoring at each step. Table 4 presents a summary of the performance analysis for each feature importance method with its corresponding ML models. The table showed that the highest accuracy on all experiments and feature importance stages recorded 84.4% for the RF method with the NB algorithm. The second-best accuracy with the NB algorithm was 83.8% for the Chi\({}^{2}\) method. The SVM algorithm also achieved good results, with 82.6% and 81.5% on RF and Chi\({}^{2}\), respectively. The provided accuracy provides promising results based on the challenge of pure Arabic sentiment analysis documents and terms collected from different domains [1,51]. In Table 5, the number of features and experiments that are conducted on the dataset with different feature selection methods are explained. The table shows more than 3 million extracted features during the testing of data with about 4423 experiments on all feature selection methods. \begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline  & & & \multicolumn{4}{c}{**RF**} & \\ \hline NB & SVM & LR & LDA & RF & KNN & CART & XGB \\ \hline \multicolumn{8}{c}{**50.1**} \\ \hline NB & SVM & LR & LDA & RF & KNN & CART & XGB \\ \hline \multicolumn{8}{c}{**50.2**} \\ \hline NB & SVM & LR & LDA & RF & KNN & CART & XGB \\ \hline \multicolumn{8}{c}{**50.3**} \\ \hline
50.4\% & 58.7\% & 58.7\% & 58.5\% & 52.4\% & 49.5\% & 49.4\% & 49.6\% \\ \hline \hline \end{tabular}
\end{table} 
Table 4: Analysis of accuracy for different feature importance methods with ML models. Figure 6: Accuracy of Pearson feature selection method with ML models. In Table 6, the top 10 accuracies for the experimental results that are executed on all feature importance methods with ML models are provided. The explanation of these results was intended to study the efficiency for feature importance models in all sentiment analysis documents and terms. For each feature \(k=100\), the orientation of polarities were mapped into two classes whether they were positive or negative. The feature importance method was applied to score the accuracy for detecting positive or negative polarities using different ML models. After scoring the first batch of \(k\) features, an additional \(k+1\) feature is added to form an accumulative feature where the FFF-SA algorithm is executed again. As stated in the table, the top 10 accuracies are recorded with the NB model with both RF and Chi\({}^{2}\) feature importance methods. The best seven results are recorded with the RF method with a different number of accumulative \(k\) features. Based on these experiments, it is clear that the NB model with the RF feature importance method scored the best results on Arabic sentiment analysis terms. The conducted results in this paper showed promising results on Arabic sentiment analysis documents and terms that are considered a challenge in data preprocessing, feature extraction, and identification of sentiment polarities. In addition, the Arabic language contains several linguistic terms and colloquial terminologies that are difficult to preprocess and analyze. Figure 7 shows the confusion matrix measured for the last experiments for the final winning model. In Table 7, a comparison is performed with recent methodologies and frameworks conducted on the Arabic dataset to measure the performance of each model and the annotated algorithm.