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

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. This chapter formulates the problem of dynamically identifying key topics with proper labels from COVID-19 tweets to provide an overview of wider public opinion. It proposes a framework that automatically identifies the key topics with labels from tweets using the top Unigram feature of aspect terms cluster from Latent Dirichlet Allocation generated topics. COVID-19-related tweets can be considered helpful in delivering meaningful topics to better understand ideas and highlight human conversations. The LDA model for topic extraction has been used by many researchers. Identifying the topic of the tweets of social media platforms such as Twitter can provide meaningful insights into understanding people's ideas, which can be difficult to achieve with traditional strategies, such as manual methods. Regarding labeled topics using the Unigram feature of aspect terms in COVID-19 tweets, it is possible to notice several issues related to needs and highlighting conversations of people or users on Twitter.