Currently , the sentiment analysis of Tweets related to COVID-19 has mainly focused on identifying specific emotions ( e.g. , angry , fear , anxiety , etc . ) and top topics . However , not much study has dedicated to investigating the correlations between Tweet sentiment , public health policies and the on - going progress of COVID-19 . Moreover , machine learning methods are not widely used for sentiment analysis in these studies . Our study combined the benefits of Twitter data and machine learning methods - being real - time and of large scale , and thus more accurate , and carried out a correlation study that gave researchers quantified insights into the specific factors that triggered Tweet sentiment changes . Furthermore , most of the existing studies focus on providing an overview of public sentiment change across the globe or concentrate on country - level investigation . Our study seeks to provide a city - level analysis that could be more helpful for local governments to implement policy interventions that take the context of each city into consideration .