People utilize social media platforms such as Twitter for social networking and to communicate their thoughts, feelings, and ideas with others. During the COVID-19 pandemic, Malaysians took to social media to express their feelings. This research aims to understand Malaysian Twitter users' discussions and psychological reactions to the COVID-19 pandemic. Supervised Machine learning technique was used to analyze 1,645 Tweets (written both in Malay and English) related to COVID-19 between 1st January 2020 and 7th June 2021. The tweets were pre-processed and then classified using Support Vector Machine (SVM) algorithm for the sentiment analysis. The classification results matched 6 basic emotions from Paul Ekman Model which are anger, fear, happy, love, sadness, and surprise. From the sentiment analysis, it was found that anger towards people who violated the Standard Operating Procedure (SOP) during Movement Control Order (MCO), political issues, and the issue of non-essential companies and factories disregarding the order to cease operation during MCO is dominant. This research may aid health professionals and researchers in better understanding Malaysian citizens' possible reactions during massive health crisis.
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