Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers-A study to show how popularity is affecting accuracy in social media.
Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers-A study to show how popularity is affecting accuracy in social media.
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DOI：
10.1016/j.asoc.2020.106754
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发表时间：
2020-12
影响因子：
8.7
通讯作者：
Hassanien AE
中科院分区：
文献类型：
--
作者：
Chakraborty K;Bhatia S;Bhattacharyya S;Platos J;Bag R;Hassanien AE
COVID-19 最初被称为 2019 年冠状病毒病，已于 2020 年 3 月 11 日被世界卫生组织 (WHO) 宣布为大流行病。每个国家都面临着前所未有的压力，需要通过评估病例和正确利用现有资源来制定控制人口的迫切要求。全球范围内病例数量的迅速激增，引起了人们的恐慌、恐惧和焦虑。研究发现，全球人口的精神和身体健康状况与这种大流行病成正比。目前的情况是，截至2020年8月27日，全球已有超过2400万人检测呈阳性。因此，现在需要采取不同的措施，通过揭开相关事实和信息的神秘面纱来保护国家。本文旨在揭示这样一个事实：包含与 COVID-19 和 WHO 相关的所有句柄的推文未能成功地指导人们应对这一大流行病的爆发。这项研究分析了大流行期间收集的两种类型的推文。在一个案例中，我们对 2019 年 1 月 1 日至 2020 年 3 月 23 日期间转发次数最多的约 2.3 万条推文进行了分析，观察结果表明，最大数量的推文描绘了中性或负面情绪。另一方面，对包含 2019 年 12 月至 2020 年 5 月期间收集的 226,668 条推文的数据集进行了分析，对比显示，网民发布的正面和中立推文数量最多。研究表明，尽管人们发布的推文大多是关于 COVID-19 的正面推文，但网民却忙于转发负面推文，并且在 WordCloud 或使用推文中的词频计算中找不到有用的单词。这些说法已通过使用深度学习分类器的拟议模型得到验证，可接受的准确度高达 81%。除此之外，作者还提出了实现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。该模型的允许准确率高达 79%。这篇论文揭示了相关推文未能为人们提供有关 COVID-19 大流行的指导。这项研究分析了大流行期间收集的两种类型的推文。研究表明，WordCloud 中没有发现有用的单词，推文中的词频也没有。所提出的深度学习分类器模型对声明进行了验证，准确率高达 81%。设计的基于高斯隶属度的模糊规则库可以正确识别推文中的情绪。
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%. This paper reveals that related tweets failed to guide people on COVID-19 pandemic. This study analyzes two types of tweets gathered during the pandemic times. The research demonstrates that no useful words are found in WordCloud or word frequency in tweets. Claims are validated by a proposed deep learning classifier model yielding accuracy up to 81%. A designed Gaussian membership based fuzzy rule base correctly identifies sentiments from tweets.
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作者：
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