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This paper formulates the problem of dynamically identifying key topics withproper labels from COVID-19 Tweets to provide an overview of wider publicopinion. Nowadays, social media is one of the best ways to connect peoplethrough Internet technology, which is also considered an essential part of ourdaily lives. In late December 2019, an outbreak of the novel coronavirus,COVID-19 was reported, and the World Health Organization declared an emergencydue to its rapid spread all over the world. The COVID-19 epidemic has affectedthe use of social media by many people across the globe. Twitter is one of themost influential social media services, which has seen a dramatic increase inits use from the epidemic. Thus dynamic extraction of specific topics withlabels from tweets of COVID-19 is a challenging issue for highlightingconversation instead of manual topic labeling approach. In this paper, wepropose a framework that automatically identifies the key topics with labelsfrom the tweets using the top Unigram feature of aspect terms cluster fromLatent Dirichlet Allocation (LDA) generated topics. Our experiment result showsthat this dynamic topic identification and labeling approach is effectivehaving the accuracy of 85.48\% with respect to the manual static approach.

A Dynamic Topic Identification and Labeling Approach of COVID-19 Tweets