Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Articles Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Sentiment Analysis of COVID-19 tweets by Deep Learning ClassifiersA study to show how popularity is affecting accuracy in social media Subject The topic of the resource COVID-19; Deep learning; Emotional intelligence; Fuzzy rule; Gaussian membership function; Sentiment analysis; Tweets; WHO Description An account of the resource 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%. 2020 Elsevier B.V. Creator An entity primarily responsible for making the resource Chakraborty K.; Bhatia S.; Bhattacharyya S.; Platos J.; Bag R.; Hassanien A.E. Source A related resource from which the described resource is derived Applied Soft Computing Journal, Vol-97 Publisher An entity responsible for making the resource available Elsevier Ltd Date A point or period of time associated with an event in the lifecycle of the resource 2020-01-01 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1016/j.asoc.2020.106754" target="_blank" rel="noreferrer noopener">https://doi.org/10.1016/j.asoc.2020.106754</a> <br /><br /><a href="https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092457474&amp;doi=10.1016%2Fj.asoc.2020.106754&amp;partnerID=40&amp;md5=d9a3624bc2652408cc01d63cd0f9b828" target="_blank" rel="noreferrer noopener">https://www.scopus.com/inward/record.uri?eid=2-s2.0-85092457474&amp;doi=10.1016%2fj.asoc.2020.106754&amp;partnerID=40&amp;md5=d9a3624bc2652408cc01d63cd0f9b828</a> Rights Information about rights held in and over the resource All Open Access; Green Open Access Relation A related resource ISSN: 15684946 Format The file format, physical medium, or dimensions of the resource Online Language A language of the resource English Type The nature or genre of the resource Article Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Chakraborty K., Department of Computer Science and Engineering, Supreme Knowledge Foundation Group of Institutions, Mankundu, India; Bhatia S., Department of Information Systems, College of Computer Science and Information Technology, King Faisal University, Saudi Arabia; Bhattacharyya S., Department of Computer Science and Engineering, CHRIST (Deemed to be University), Bangalore, India; Platos J., Faculty of Electrical Engineering and Computer Science, VSB Technical University of Ostrava, Czech Republic; Bag R., Department of Computer Science and Engineering, Supreme Knowledge Foundation Group of Institutions, Mankundu, India; Hassanien A.E., Faculty of Computers and Artificial Intelligence, Cairo University, Egypt