Pandey Digvijay, Pradhan Bandinee
Department of Technical Education, IET, Dr. A.P.J. Abdul Kalam Technical University Uttar Pradesh, Lucknow 226021, India.
PDPU, Gandhinagar, India.
Heliyon. 2022 Aug;8(8):e09994. doi: 10.1016/j.heliyon.2022.e09994. Epub 2022 Jul 19.
COVID-19 outbreak has caused a high number of casualties and is an unprecedented public health emergency. Twitter has emerged as a major platform for public interactions, giving opportunity to researchers for understanding public response to the outbreak. The researchers analyzed 100,000 tweets with hashtags #coronavirus, #coronavirusoutbreak, #coronavirusPandemic, #COVID19, #COVID-19, #epitwitter, #ihavecorona, #StayHomeStaySafe, #TestTraceIsolate. Programming languages such as Python, Google NLP, and NVivo are used for sentiment analysis and thematic analysis. The result showed 29.61% tweets were attached to positive sentiments, 29.49% mixed sentiments, 23.23 % neutral sentiments and 18.069% negative sentiments. Popular keywords include "cases", "home", "people" and "help". We identified "30" such topics and categorized them into "three" themes: Public Health, COVID-19 around the world and Number of Cases/Death. This study shows twitter data and NLP approach can be utilized for studies related to public discussion and sentiments during the COVID-19 outbreak. Real time analysis can help reduce the false messages and increase the efficiency in proving the right guidelines for people.
新冠疫情已造成大量人员伤亡，是一场前所未有的突发公共卫生事件。推特已成为公众互动的主要平台，为研究人员提供了了解公众对疫情反应的机会。研究人员分析了10万条带有#冠状病毒、#新冠疫情爆发、#冠状病毒大流行、#新冠病毒19、#新冠-19、#疫情推特、#我感染了新冠、#居家安全、#检测追踪隔离等标签的推文。使用了Python、谷歌自然语言处理和NVivo等编程语言进行情感分析和主题分析。结果显示，29.61%的推文带有积极情绪，29.49%为混合情绪，23.23%为中性情绪，18.069%为消极情绪。热门关键词包括“病例”“家”“人们”和“帮助”。我们识别出“30”个这样的主题，并将它们归为“三个”主题：公共卫生、全球新冠疫情以及病例/死亡数量。这项研究表明，推特数据和自然语言处理方法可用于与新冠疫情爆发期间公众讨论和情绪相关的研究。实时分析有助于减少虚假信息，并提高为人们提供正确指导方针的效率。
