The COVID-19 pandemic has imposed substantial challenges globally, and many have voiced their thoughts on social media about the unfolding crisis. This study performed a sentiment analysis (SA) on Twitter posts, to assess the general public’s perspective on the pandemic. Our primary objective was to compare the performance of two frequently used algorithms, support vector machine (SVM) and Naive Bayes classification (NBC), in performing SA, which is a novel direction. The results showed that the more sophisticated SVM algorithm obtained 81% accuracy compared with the 74% achieved by NBC. Consequently, SVM may be used to assess and acquire improved public SA from Twitter.