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An analysis of attitude of general public toward COVID-19 crises - sentimental analysis and a topic modeling study

机译:对Covid-19危机的公众态度分析 - 感伤分析及主题建模研究

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Purpose - It has been eight months into the global pandemic health crises COVID-19, yet the severity of the crises is just getting worse in many parts of the world. At this stage, it is essential to understand and observe the general attitude of the public toward COVID crises and the major concerns the public has voiced out and how it varies across months. Understanding the impact that the COVID-19 crises have created also helps policymakers and health-care organizations access the primary steps that need to be taken for the welfare of the community. The purpose of this study is to understand the general public's response towards COVID-19 crises and the major issues that concerns them. Design/methodology/approach - For the analysis, data were collected from Twitter. Tweets regarding COVID-19 crises were collected from February 1, 2020, to June 27, 2020. In all, 433,195 tweets were used for this study. Natural language processing (NLP), which is a part of Machine learning, was used for this study. NLP was used to track the changes in the general public's sentiment toward COVID-19 crises and LDA was used to understand the issues that shape the general public's sentiments the crises time. Using Python library Wordcloud, the authors further derived how the primary concerns regarding COVID crises various from February to June of the year 2020. Findings - This study was conducted in two parts. Study 1 results showed that the attitude of the general public toward COVID crises was reasonably neutral at the beginning of the crises (Month of February). As the crises become severe, the sentiments toward COVID increasingly become negative yet a considerable percentage of neutral sentiments existed even at the peak time of the crises. Study 2 finds out that issues including the severity of the disease. Precautionary measures need to be taken, and Personal issues like unemployment and traveling during the pandemic time were identified as the public's primary concerns. Originality/value - The research adds value to the literature on understanding the major issues and concerns, the public voices out about the current ongoing pandemic. To the best of the authors' knowledge, this is the first study with an extended period of timeframe (Five months). In this research, the authors have collected data till June for analysis that makes the results and findings more relevant to the current time.
机译:目的 - 全球大流行健康危机Covid-19已经八个月了,但危机的严重程度在世界许多地方越来越差。在这个阶段,必须了解和观察公众对Covid危机的一般态度,并且公众发出的主要担忧以及如何在几个月内变化。了解Covid-19危机所创造的影响也有助于政策制定者和医疗保健组织获得社区福利需要采取的主要步骤。本研究的目的是了解公众对Covid-19危机的回应以及涉及他们的主要问题。设计/方法/方法 - 用于分析,从Twitter收集数据。有关Covid-19危机的推文于2020年2月1日至6月27日收集到2020年6月27日。总而言之,433,195次推文用于本研究。本研究使用是机器学习的一部分的自然语言处理(NLP)。 NLP被用来跟踪一般公众对Covid-19危机的情绪的变化,LDA被用来了解旨在塑造危机时间的危机的问题。使用Python Library WordCloud,该作者进一步派生了与2020年2月至6月的Covid危机的主要问题。发现 - 这项研究是分两部分进行的。研究1结果表明,一般公众对Covid危机的态度在危机开始时具有相当中立的(2月份)。由于危机变得严重,即使在危机的高峰时间,也越来越多地变得负面变为负数百分比的中性情绪。研究表明发现包括疾病严重程度的问题。需要采取预防措施,并且在大流行期间出现失业和旅行等个人问题被确定为公众的主要问题。原创性/价值 - 该研究为理解主要问题和疑虑的文献增加了价值,公众声音关于当前持续的大流行。据作者的知识中,这是第一次在延长时间框架(五个月)的研究。在这项研究中,作者收集了数据直到6月份进行分析,使结果和结果与当前时间更相关。

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