## 5 Discussion  
We conducted experiments on a new benchmark Arabic text dataset, namely, MAWQIF, which was collected in December 2022 [30]. The MAWQIF contains three topics: sentiment, sarcasm, and stance, as well as the multilabels of the three topics. The proposed approach is evaluated on the dataset for each topic label and then for multilabel classes. Performing feature selection using GA and metaheuristics with ML base models and TF-IDF and count vectorizers of unigram features can impact the classification performance in two ways. Firstly, it decreases the vector size during the feature representation and reduces the model complexity and size. Secondly, it can also reduce noise and improve the performance of ML models. We used three classifiers called the ETC, LRC, and RFC with both CountVectorizer and TfidfVectorizer. In all settings, the unigram for extracting features is also used. The F1-score is selected as an appropriate metric because the dataset is imbalanced, and the accuracy is not suitable in this case. Furthermore, the precision and recall are discounted because it is already used for computing the F1-score. The ensemble learning technique is adopted due to its ability to combine the power of multiple models to make better decisions than individual models. Regarding the sarcasm topic, all five configuration settings enhanced the performance, as seen in Table 8. Remarkably in sentiment, the ETC with CountVectorizer has the highest result, improving the F1-score from 79.17% to 80.88%, followed by the RFC with CountVectorizer, which boosts the F1-score from 75.81% to 77.26% and also better performance than the LRC. For the classification results of the sarcasm topic shown in Table 10, the feature selection increases the F1-score of the LRC from 93.52% to 94.21%, indicating the effectiveness of the feature selection for improving the classification performance of Arabic sarcasm labels. The classification performance of other models is also slightly increased, but they achieved a higher F1-score than the LRC. In the third scenario, stance classes are classified with and without feature selection, as presented in Table 12. The results showed a slight increase due to the imbalanced class problem. In the last experiment, Tables 14 and 15 focused on the main goal of this study, which is how the GA-based feature selection with ensemble learning can improve the performance of multilabel classification results from 65.62% to 68.76% on the original dataset using ETC with feature selection. In addition, the data augmentation with feature selection improves the result of the F1-score to 68.80% using the same classifier. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Task Scenario** & \begin{tabular}{c} **F1-Score without Feature** \\ **Selection (\%)** \\ \end{tabular} & \
\begin{tabular}{c} **F1-Score with Feature** \\ **Selection (\%)** \\ \end{tabular} &
\begin{tabular}{c} **F1-Score with Feature Selection and** \\ **Data Augmentation (\%)** \\ \end{tabular} \\ \hline Sentiment & 79.17 & 80.88 & 86.51 \\ Sarcasm & 95.38 & 95.38 & 96.52 \\ Stance & 90.73 & 94.11 & 95.50 \\ Multilabel & 65.62 & 68.76 & 68.80 \\ \hline \hline \end{tabular}
\end{table} 
Table 16: The highest F1-score results of each topic and multilabel classes with and without feature selection and optimization method. \begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classifier Model** & **Feature** & **Vector** & **F1-Score with Feature Selection (\%)** \\ \hline ETC & Unigram & CountVectorizer & 68.80 \\ ETC & Unigram & TfidfVectorizer & 66.54 \\ LRC & Unigram & CountVectorizer & 65.43 \\ RFC & Unigram & CountVectorizer & 65.00 \\ RFC & Unigram & TfidfVectorizer & 66.78 \\ \hline \hline \end{tabular}
\end{table} 
Table 15: The results of the five configuration settings for the augmented multilabel scenario with and without the feature selection and optimization method.