## 1 Introduction
[20] stated that conducted sentiment analysis on posts of Indonesian locals about governmental performance during the COVID-19 pandemic can be used as a DSS for the Indonesian government to improve its performance in handling COVID-19. Evolutionary Algorithms (EAs) are defined as techniques applied in solutions to complex problems [21; 22]. EA has the ability to obtain the most suitable solutions with less computational effort than other methods [23]. Furthermore, it can be easily encoded the candidate solutions and applied to different problems, namely, mono-objective and multi-objective [24]. Therefore, they have been applied to various domains of optimization problems due to their independence from problem type [25; 26]. Different EAs were applied to SA, such as Decision Trees (DT), k-Nearest Neighbor (KNN), Naive Bayes (NB), and Support Vector Machines (SVM) as per the method in [13]. One of the well-known EAs that was used recently in the literature is the Whale Optimization Algorithm (WOA). The algorithm employed in different works and proves its superiority, especially in SA. Few works have been introduced, for example, the authors of [27] proposed an SA approach using WOA for feature selection mechanism, while the work in [28] provided a sentiment classification method in the social media environment by utilizing WOA. Therefore, in this work, we presented an evolutionary approach for improving government decisions through using a decision support system and sentiment analysis and, further, combined them with a metaheuristic algorithm and SVM. The approach utilized the combination of WOA and SVM for the sake of hyperparameter tuning and features weighting. Therefore, studying people attitudes, feelings, and concerns toward the government new decisions and regulations can enhance governments' decisions in many different ways, it may help decision-makers to make any necessary adjustments needed to mitigate the number of cases and reduce its impact on different aspects of life to guarantee people support and commitment. In addition, if the Government senses from the population feedback any negative attitudes toward its regulations, it can take proactive action to avoid further deterioration in the economy; employment; or any risks of violations, crimes, violence, or even the well-being of individuals and societies. Moreover, our proposed approach can enhance the decision-makers' countermeasures and provide them with an easily adaptable methodology with a set of general guidelines that could facilitate complex decision-making processes based on people sentiment and via investigating and ranking the most important set of key features for COVID-19 posts. This clear and structured framework can be adapted to different scenarios and contexts in the future to deal with similar conditions (COVID-19) quickly and efficiently. The following points illustrated the main contributions of this study:  
* Combined the decision support system and sentiment analysis mechanism to enhance government decisions during the COVID-19 pandemic and other similar situations. * The WOA-SVM hybrid framework was proposed for hyperparameter optimization and features weighting. * Investigate the behavior changes of people's opinions in two different periods, first and latter regulations. * Study and analyze these behaviors to provide better countermeasures, alert decision-makers in case of possible threats, and perform proactive actions to handle the current pandemic situation. * Five different versions were generated from each dataset, consisting of various feature extraction and stemming techniques. * Identify the most relevant features to improve the decision support system and sentiment prediction. The remainder of the paper is divided as follows. In Section 2, a literature review on the decision support system during the COVID-19 is presented. Background of used methods and concepts introduced in Section 3. The proposed approach is described in Section 4.