@misc{10481/71355,
year = {2021},
month = {9},
url = {http://hdl.handle.net/10481/71355},
abstract = {The world has witnessed recently a global outbreak of coronavirus disease (COVID-19).
This pandemic has affected many countries and has resulted in worldwide health concerns, thus
governments are attempting to reduce its spread and impact on different aspects of life such as health,
economics, education, and politics by making emergent decisions and policies (e.g., lockdown and
social distancing). These new regulations influenced people’s daily life and cast significant burdens,
concerns, and disparities on various population groups. Taking the wrong actions and enforcing bad
decisions by some countries result in increasing the contagion rate and more catastrophic results.
People start to post their opinions and feelings about their government’s decisions on different social
media networks, and the data received through these platforms present a very useful source of
information that affects how governments perceive and cope with the current the pandemic. Jordan
was one of the top affected countries. In this paper, we proposed a decision support system based on
the sentiment analysis mechanism by combining support vector machines with a whale optimization
algorithm for automatically tuning the hyperparameters and performing feature weighting. The
work is based on a hybrid evolutionary approach that aims to perform sentiment analysis combined
with a decision support system to study people’s posts on Facebook to investigate their attitudes
and feelings toward the government’s decisions during the pandemic. The government regulations
were divided into two periods: the first and latter regulations. Studying public sentiments during
these periods allows decision-makers in the government to sense people’s feelings, alert them in
case of possible threats, and help in making proactive actions if needed to better handle the current
pandemic situation. Five different versions were generated for each of the two collected datasets. The
results demonstrate the superiority of the proposedWhale Optimization Algorithm & Support Vector
Machines (WOA-SVM) against other metaheuristic algorithms and standard classification models as
WOA-SVM has achieved 78.78% in terms of accuracy and 84.64% in term of f-measure, while other
standard classification models such as NB, k-NN, J84, and SVMachieved an accuracy of 69.25%, 69.78%,
70.17%, and 69.29%, respectively, with 64.15%, 62.90%, 60.51%, and 59.09% F-measure. Moreover, when
comparing our proposedWOA-SVMapproachwith othermetaheuristic algorithms,which are GA-SVM,
PSO-SVM, and MVO-SVM, WOA-SVM proved to outperform the other approaches with results of
78.78% in terms of accuracy and 84.64% in terms of F-measure. Further, we investigate and analyze the
most relevant features and their effect to improve the decision support system of government decisions.},
publisher = {MDPI},
keywords = {Decision support system},
keywords = {COVID-19},
keywords = {Sentiment analysis},
keywords = {Support vector machine},
keywords = {SVM},
keywords = {Whale optimization algorithm},
keywords = {WOA},
title = {An Evolutionary-Based Sentiment Analysis Approach for Enhancing Government Decisions during COVID-19 Pandemic: The Case of Jordan},
doi = {10.3390/app11199080},
author = {Obiedat, Ruba and Al-Zoubi, Ala´ M.},
}