Public Perception of the COVID-19 Pandemic on Twitter: Sentiment Analysis and Topic Modeling Study.
Public Perception of the COVID-19 Pandemic on Twitter: Sentiment Analysis and Topic Modeling Study.
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DOI：
10.2196/21978
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发表时间：
2020-11-11
影响因子：
8.5
通讯作者：
Skunkan Y
中科院分区：
文献类型：
--
作者：
Boon-Itt S;Skunkan Y
COVID-19是一种科学和医学上新颖的疾病，由于尚未得到持续和深入的研究，因此尚未完全了解。在COVID-19疫情研究的空白中，缺乏足够的信息监测数据。本研究的目的是提高公众对COVID-19大流行趋势的认识，并揭示Twitter用户在大流行期间用英语发布的有意义的关注主题。在推特上进行数据挖掘，收集了2020年12月13日至3月9日期间与COVID-19相关的107,990条推文。分析包括关键词的频率、情感分析和主题建模，以识别和探索随时间变化的讨论主题。使用自然语言处理方法和潜在狄利克雷分配算法来识别最常见的推文主题，并基于关键词分析对聚类进行分类和识别主题。结果表明，公众对COVID-19大流行的认识和关注主要有三个方面。首先，新冠肺炎的传播趋势和症状可以分为三个阶段。第二，情绪分析结果显示，国民对新冠肺炎的看法是消极的。第三，在主题建模的基础上，将与COVID-19疫情相关的主题分为三类：COVID-19大流行突发事件、如何控制和COVID-19报告。情绪分析和主题建模可以提供有关社交媒体上关于COVID-19大流行讨论趋势的有用信息，以及调查COVID-19危机的替代视角，这已经引起了相当大的公众意识。这项研究表明，推特是了解公众对COVID-19的关注和公众意识的良好沟通渠道。这些发现可以帮助卫生部门传达信息，以减轻公众对这种疾病的具体担忧。
COVID-19 is a scientifically and medically novel disease that is not fully understood because it has yet to be consistently and deeply studied. Among the gaps in research on the COVID-19 outbreak, there is a lack of sufficient infoveillance data. The aim of this study was to increase understanding of public awareness of COVID-19 pandemic trends and uncover meaningful themes of concern posted by Twitter users in the English language during the pandemic. Data mining was conducted on Twitter to collect a total of 107,990 tweets related to COVID-19 between December 13 and March 9, 2020. The analyses included frequency of keywords, sentiment analysis, and topic modeling to identify and explore discussion topics over time. A natural language processing approach and the latent Dirichlet allocation algorithm were used to identify the most common tweet topics as well as to categorize clusters and identify themes based on the keyword analysis. The results indicate three main aspects of public awareness and concern regarding the COVID-19 pandemic. First, the trend of the spread and symptoms of COVID-19 can be divided into three stages. Second, the results of the sentiment analysis showed that people have a negative outlook toward COVID-19. Third, based on topic modeling, the themes relating to COVID-19 and the outbreak were divided into three categories: the COVID-19 pandemic emergency, how to control COVID-19, and reports on COVID-19. Sentiment analysis and topic modeling can produce useful information about the trends in the discussion of the COVID-19 pandemic on social media as well as alternative perspectives to investigate the COVID-19 crisis, which has created considerable public awareness. This study shows that Twitter is a good communication channel for understanding both public concern and public awareness about COVID-19. These findings can help health departments communicate information to alleviate specific public concerns about the disease.
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影响因子：
3.7
作者：
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通讯作者：
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