Public Sentiment Analysis and Topic Modeling Regarding COVID-19’s Three Waves of Total Lockdown: A Case Study on Movement Control Order in Malaysia
A. H. Alamoodi, Mohammed Rashad Baker, O. S. Albahri, B. B. Zaidan, A. A. Zaidan, Wing‐Kwong Wong, Salem Garfan, A. S. Albahri, Miguel Á. Alonso, Ali Najm Jasim, M. J. Baqer
Abstract
The COVID-19 pandemic has affected many aspects of human life. The pandemic not only caused millions of fatalities and problems but also changed public sentiment and behavior. Owing to the magnitude of this pandemic, governments worldwide adopted full lockdown measures that attracted much discussion on social media platforms. To investigate the effects of these lockdown measures, this study performed sentiment analysis and latent Dirichlet allocation topic modeling on textual data from Twitter published during the three lockdown waves in Malaysia between 2020 and 2021. Three lockdown measures were identified, the related data for the first two weeks of each lockdown were collected and analysed to understand the public sentiment. The changes between these lockdowns were identified, and the latent topics were highlighted. Most of the public sentiment focused on the first lockdown Alamoodi et al.: Public Sentiment Analysis and Topic Modeling Regarding COVID-19's Three Waves of Total Lockdown: A Case Study on Movement Control Order in Malaysia