  This paper formulates the problem of dynamically identifying key topics with
proper labels from COVID-19 Tweets to provide an overview of wider public
opinion. Nowadays, social media is one of the best ways to connect people
through Internet technology, which is also considered an essential part of our
daily lives. In late December 2019, an outbreak of the novel coronavirus,
COVID-19 was reported, and the World Health Organization declared an emergency
due to its rapid spread all over the world. The COVID-19 epidemic has affected
the use of social media by many people across the globe. Twitter is one of the
most influential social media services, which has seen a dramatic increase in
its use from the epidemic. Thus dynamic extraction of specific topics with
labels from tweets of COVID-19 is a challenging issue for highlighting
conversation instead of manual topic labeling approach. In this paper, we
propose a framework that automatically identifies the key topics with labels
from the tweets using the top Unigram feature of aspect terms cluster from
Latent Dirichlet Allocation (LDA) generated topics. Our experiment result shows
that this dynamic topic identification and labeling approach is effective
having the accuracy of 85.48\% with respect to the manual static approach.
