Department of CSE, Bangladesh University of Engineering and Technology, West Palashi, Dhaka 1205, Bangladesh.
College of Science and Engineering, Hamad Bin Khalifa University, Doha 34110, Qatar.
Stud Health Technol Inform. 2022 Jun 6;290:709-713. doi: 10.3233/SHTI220170.
COVID-19 pandemic is taking a toll on the social, economic, and psychological well-being of people. During this pandemic period, people have utilized social media platforms (e.g., Twitter) to communicate with each other and share their concerns and updates. In this study, we analyzed nearly 25M COVID-19 related tweets generated from 20 different countries and 28 states of USA over a month. We leveraged sentiment analysis and topic modeling over this collection and clustered different geolocations based on their sentiment. Our analysis identified 3 geo-clusters (country- and US state-based) based on public sentiment and discovered 15 topics that could be summarized under three main themes: government actions, medical issues, and people's mood during the home quarantine. The proposed computational pipeline has adequately captured the Twitter population's emotion and sentiment, which could be linked to government/policy makers' decisions and actions (or lack thereof). We believe that our analysis pipeline could be instrumental for the policymakers in sensing the public emotion/support with respect to the interventions/actions taken, for example, by the government instrumentality.
新冠疫情大流行对人们的社会、经济和心理健康造成了影响。在疫情期间，人们利用社交媒体平台（如 Twitter）相互交流，并分享他们的担忧和最新情况。在这项研究中，我们分析了来自 20 个不同国家和美国 28 个州的近 2500 万条与新冠疫情相关的推文，这些推文在一个月内生成。我们利用情感分析和主题建模对这些推文进行了分析，并根据情感将不同的地理位置聚类。我们的分析根据公众情绪确定了 3 个地理聚类（基于国家和美国州的聚类），并发现了 15 个可以归纳为三个主题的主题：政府行动、医疗问题和居家隔离期间人们的情绪。提出的计算管道充分捕捉了 Twitter 人群的情感，可以将其与政府/政策制定者的决策和行动（或缺乏行动）联系起来。我们相信，我们的分析管道可以为政策制定者提供有关干预措施/行动的公众情绪/支持的信息，例如，政府机构的干预措施。
