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.
翻译:本文阐述了以COVID-19 Tweets的恰当标签动态识别关键主题以提供更广泛的公众舆论概览的问题。如今,社交媒体是通过互联网技术连接人们的最佳途径之一,这也是我们日常生活的重要组成部分。在2019年12月底,新创的科罗纳病毒(COVID-19)爆发了,世界卫生组织由于它迅速蔓延到世界各地而宣布了紧急情况。COVID-19流行病影响了全球许多人使用社交媒体。Twitter是最具影响力的社会媒体服务之一,其使用从该流行病中急剧增加。因此,动态提取带有CVID-19的推特标签的具体专题对于突出对话而不是手动标注方法来说是一个具有挑战性的问题。在这份文件中,我们提出了一个框架,用Litetent Dirichlet分配(LDA)的顶级术语组群集的顶级Unigram特征自动识别关键专题。我们的实验结果表明,这种动态专题识别和标签方法对85.48号手册的准确性是有效的。