## 5 Experiment and Results
### Feature Importance Analysis
\begin{table}
\begin{tabular}{c c c c c c c c} \hline \hline \multirow{2}{*}{**Datasets**} & \multicolumn{2}{c}{**GA-SVM**} & \multicolumn{2}{c}{**PSO-SVM**} & \multicolumn{2}{c}{**MVO-SVM**} & \multicolumn{2}{c}{**WOA-SVM**} \\ \cline{2-9}  & **Avg** & **Std** & **Avg** & **Std** & **Avg** & **Std** & **Avg** & **Std** \\ \hline Data 6 & 79.98\% & 3.75\% & 81.53\% & 5.05\% & 82.29\% & 3.76\% & **84.48\%** & 3.00\% \\ Data 7 & 84.02\% & 3.07\% & 84.01\% & 2.41\% & 84.22\% & 2.22\% & **84.64\%** & 2.16\% \\ Data 8 & 77.66\% & 4.60\% & 83.06\% & 2.68\% & 82.84\% & 3.58\% & **83.58\%** & 2.46\% \\ Data 9 & 82.80\% & 2.13\% & 82.64\% & 2.10\% & **84.13\%** & 1.74\% & 83.55\% & 2.32\% \\ Data 10 & 82.88\% & 2.24\% & 83.93\% & 2.71\% & 84.32\% & 2.17\% & **84.59\%** & 2.01\% \\ \hline \hline \end{tabular}
\end{table} 
Table 11: F-measure results of GA-SVM, PSO-SVM, WOA-SVM, and MVO-SVM for Latter-rules datasets. clarify that. People are now feeling monotonous about the circumstances and they are waiting for new regulations to change the situation, while some individuals remain interested in the disease and its effect as shown in features like 'Infections', 'Contact', 'Cases', and 'Spread'. Overall, we can observe that people were first interested in the disease and its impact, while in the second period, they lost interest and consecrated more on other factors, including, work, school, making money, and regulations. Furthermore, we can see that there were features that were important in one data and not in the other, such as 'Fever' and 'Lockdown'. Therefore, decision-makers could take into account some of these features and act directly and quickly to make accurate decisions. Analyzing these features that are extracted from public opinions, concerns, feedback, and requests will help and alert the government about the number of violations that occurred in specific locations, thus taking action immediately. In the same context, this can also give an early alarm for their governments about the citizens' negative attitudes or any possible aggressive reactions against the governments' regulations, which may threaten the country's safety and cause great damage to the current pandemic situation. Especially in the latter period where people became fed up with the lockdown situation and were more concerned about other issues such as 'Work', 'Salary', 'Food', and education, they started asking for reopening of different life aspects and became more dissatisfied about their governments' regulations. All aforementioned points can be addressed by our study decision support system, via investigating and ranking the most important set of key features for COVID-19 posts. Thus, these features can be taken into consideration by governments and policy makers, give them indications about when it is necessary to adjust their regulations or make any proactive actions to minimize risks of violations, unemployment, crimes, violence or even people's depression and panic, besides trying to maximize the benefits. Therefore, our proposed DSS offers the governments' decision makers an easy adaptable methodology that could facilitate complex decision-making processes and can be adapted to different scenarios and contexts in the future. Figure 5: Features details and translation for Data 4 and 10.