Chakraborty Koyel, Bhatia Surbhi, Bhattacharyya Siddhartha, Platos Jan, Bag Rajib, Hassanien Aboul Ella
Department of Computer Science and Engineering, Supreme Knowledge Foundation Group of Institutions, Mankundu, India.
Department of Information Systems, College of Computer Science and Information Technology, King Faisal University, Saudi Arabia.
Appl Soft Comput. 2020 Dec;97:106754. doi: 10.1016/j.asoc.2020.106754. Epub 2020 Sep 28.
COVID-19 originally known as Corona VIrus Disease of 2019, has been declared as a pandemic by World Health Organization (WHO) on 11th March 2020. Unprecedented pressures have mounted on each country to make compelling requisites for controlling the population by assessing the cases and properly utilizing available resources. The rapid number of exponential cases globally has become the apprehension of panic, fear and anxiety among people. The mental and physical health of the global population is found to be directly proportional to this pandemic disease. The current situation has reported more than twenty four million people being tested positive worldwide as of 27th August, 2020. Therefore, it is the need of the hour to implement different measures to safeguard the countries by demystifying the pertinent facts and information. This paper aims to bring out the fact that tweets containing all handles related to COVID-19 and WHO have been unsuccessful in guiding people around this pandemic outbreak appositely. This study analyzes two types of tweets gathered during the pandemic times. In one case, around twenty three thousand most re-tweeted tweets within the time span from 1st Jan 2019 to 23rd March 2020 have been analyzed and observation says that the maximum number of the tweets portrays neutral or negative sentiments. On the other hand, a dataset containing 226,668 tweets collected within the time span between December 2019 and May 2020 have been analyzed which contrastingly show that there were a maximum number of positive and neutral tweets tweeted by netizens. The research demonstrates that though people have tweeted mostly positive regarding COVID-19, yet netizens were busy engrossed in re-tweeting the negative tweets and that no useful words could be found in WordCloud or computations using word frequency in tweets. The claims have been validated through a proposed model using deep learning classifiers with admissible accuracy up to 81%. Apart from these the authors have proposed the implementation of a Gaussian membership function based fuzzy rule base to correctly identify sentiments from tweets. The accuracy for the said model yields up to a permissible rate of 79%.
新冠病毒病最初被称为2019冠状病毒病，于2020年3月11日被世界卫生组织（WHO）宣布为大流行病。每个国家都承受着前所未有的压力，需要通过评估病例并合理利用现有资源来制定控制疫情的强制性要求。全球范围内呈指数级增长的病例数量已引发人们的恐慌、恐惧和焦虑。全球人口的身心健康被发现与这种大流行疾病直接相关。截至2020年8月27日，目前的情况显示全球有超过2400万人检测呈阳性。因此，当下迫切需要采取不同措施，通过揭开相关事实和信息的神秘面纱来保护各国。本文旨在揭示这样一个事实，即包含与新冠病毒病和世卫组织相关的所有账号的推文，在恰当地指导人们应对这场大流行疫情方面并不成功。本研究分析了在疫情期间收集的两种类型的推文。一方面，分析了在2019年1月1日至2020年3月23日期间约2.3万条被转发最多的推文，观察结果表明，这些推文中大部分表达的是中性或负面情绪。另一方面，分析了一个在2019年12月至2020年5月期间收集的包含226,668条推文的数据集，结果却显示网民发布的正面和中性推文数量最多。该研究表明，尽管人们关于新冠病毒病发布的大多是正面推文，但网民却忙于转发负面推文，而且在词云或使用推文中词频的计算中找不到有用的信息。这些说法已通过一个使用深度学习分类器的模型得到验证，其准确率可达81%。除此之外，作者还提出实施基于高斯隶属函数的模糊规则库，以正确识别推文中的情绪。该模型的准确率可达79%。
