  Internet forums and public social media, such as online healthcare forums,
provide a convenient channel for users (people/patients) concerned about health
issues to discuss and share information with each other. In late December 2019,
an outbreak of a novel coronavirus (infection from which results in the disease
named COVID-19) was reported, and, due to the rapid spread of the virus in
other parts of the world, the World Health Organization declared a state of
emergency. In this paper, we used automated extraction of COVID-19 related
discussions from social media and a natural language process (NLP) method based
on topic modeling to uncover various issues related to COVID-19 from public
opinions. Moreover, we also investigate how to use LSTM recurrent neural
network for sentiment classification of COVID-19 comments. Our findings shed
light on the importance of using public opinions and suitable computational
techniques to understand issues surrounding COVID-19 and to guide related
decision-making.
