## 4 Experimental Analysis and Results
### Implementation of Metaheuristic GA-Based Feature Selection
#### 4.6.5 Results of Multilabel Task Scenario  
This scenario is interesting because we study users' opinions and stances based on the three topics, and their labels are individually classified and reported in Tables 8-13. We train the ML models as multilabel classifiers. The assessment outcome of the trained models is listed in Table 14. The highlighted results with bold font represent the highest F1-score for each model. Using GA with different feature vectors, we perform the learning technique on selected features. ETC with unigram features improves the classification performance by 4.42% and 0.29% compared with the LRC and RFC. It achieves 65.62% of the F1-score for the multilabel scenario without feature selection and 68.76% of the F1-score with feature selection, according to the experimental results on the test set.  
We notice during the optimization using GA that a change in one hyper-parameter value can affect the values of other hyper-parameters. Furthermore, the optimized values of other hyper-parameters are continually reliant on the best-selected values. The GA is utilized in the metaheuristic method to find the best values for optimization. The main advantage of this research is to use the GA's capabilities through the metaheuristic method for optimizing the hyper-parameters and improving the classifier models of Arabic multilabel tasks. The highest F1-score results of each task's label and multilabel scenario with and without feature selection and optimization are given in Table 14. In addition, Table 15 visualizes the result for augmented data with good performance.  
Table 16 shows the highest result of the F1-score for the proposed approach. The results confirm the ability of the model for all scenarios, including multilabel. Hence, we can identify the percentages of improved results, which are equal to 7.34% for the sentiment class, 1.23% for the sarcasm, 4.77% for the stance classification, and 3.18% for a multilabel scenario in the case of the proposed approach with feature selection and data augmentation compared to without feature selection.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Classifier Model** & **Feature** & **Vector** & **F1-Score with Feature Selection (\%)** \\ \hline ETC & Unigram & CountVectorizer & 95.50 \\ ETC & Unigram & TfidfVectorizer & 95.10 \\ LRC & Unigram & CountVectorizer & 94.54 \\ RFC & Unigram & CountVectorizer & 95.23 \\ RFC & Unigram & TfidfVectorizer & 95.44 \\ \hline \hline \end{tabular}
\end{table}
Table 13: The results of the five configuration settings for the augmented stance task scenario with and without the 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 & 65.62 & 68.76 \\ ETC & Unigram & TfidfVectorizer & 64.79 & 64.79 \\ LRC & Unigram & CountVectorizer & 62.03 & 63.71 \\ RFC & Unigram & CountVectorizer & 64.02 & 64.54 \\ RFC & Unigram & TfidfVectorizer & 64.91 & 64.99 \\ \hline \hline \end{tabular}
\end{table}
Table 14: The results of the five configuration settings for the multilabel scenario with and without feature the selection and optimization method.