  The outbreak of the novel Coronavirus Disease (COVID-19) has greatly
influenced people's daily lives across the globe. Emergent measures and
policies (e.g., lockdown, social distancing) have been taken by governments to
combat this highly infectious disease. However, people's mental health is also
at risk due to the long-time strict social isolation rules. Hence, monitoring
people's mental health across various events and topics will be extremely
necessary for policy makers to make the appropriate decisions. On the other
hand, social media have been widely used as an outlet for people to publish and
share their personal opinions and feelings. The large scale social media posts
(e.g., tweets) provide an ideal data source to infer the mental health for
people during this pandemic period. In this work, we propose a novel framework
to analyze the topic and sentiment dynamics due to COVID-19 from the massive
social media posts. Based on a collection of 13 million tweets related to
COVID-19 over two weeks, we found that the positive sentiment shows higher
ratio than the negative sentiment during the study period. When zooming into
the topic-level analysis, we find that different aspects of COVID-19 have been
constantly discussed and show comparable sentiment polarities. Some topics like
``stay safe home" are dominated with positive sentiment. The others such as
``people death" are consistently showing negative sentiment. Overall, the
proposed framework shows insightful findings based on the analysis of the
topic-level sentiment dynamics.
