Dataset Information

Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers-A study to show how popularity is affecting accuracy in social media.

ABSTRACT: 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%.

SUBMITTER: Chakraborty K

PROVIDER: S-EPMC7521435 | biostudies-literature | 2020 Dec

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers-A study to show how popularity is affecting accuracy in social media.

Chakraborty Koyel K Bhatia Surbhi S Bhattacharyya Siddhartha S Platos Jan J Bag Rajib R Hassanien Aboul Ella AE

Applied soft computing 20200928

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 i ...[more]

PMID: 33013254

Similar Datasets

Project description:ObjectiveTo examine current vaccine sentiment on social media by constructing and analyzing semantic networks of vaccine information from highly shared websites of Twitter users in the United States; and to assist public health communication of vaccines.BackgroundVaccine hesitancy continues to contribute to suboptimal vaccination coverage in the United States, posing significant risk of disease outbreaks, yet remains poorly understood.MethodsWe constructed semantic networks of vaccine information from internet articles shared by Twitter users in the United States. We analyzed resulting network topology, compared semantic differences, and identified the most salient concepts within networks expressing positive, negative, and neutral vaccine sentiment.ResultsThe semantic network of positive vaccine sentiment demonstrated greater cohesiveness in discourse compared to the larger, less-connected network of negative vaccine sentiment. The positive sentiment network centered around parents and focused on communicating health risks and benefits, highlighting medical concepts such as measles, autism, HPV vaccine, vaccine-autism link, meningococcal disease, and MMR vaccine. In contrast, the negative network centered around children and focused on organizational bodies such as CDC, vaccine industry, doctors, mainstream media, pharmaceutical companies, and United States. The prevalence of negative vaccine sentiment was demonstrated through diverse messaging, framed around skepticism and distrust of government organizations that communicate scientific evidence supporting positive vaccine benefits.ConclusionSemantic network analysis of vaccine sentiment in online social media can enhance understanding of the scope and variability of current attitudes and beliefs toward vaccines. Our study synthesizes quantitative and qualitative evidence from an interdisciplinary approach to better understand complex drivers of vaccine hesitancy for public health communication, to improve vaccine confidence and vaccination coverage in the United States.

Dataset Information

Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers-A study to show how popularity is affecting accuracy in social media.

Publications

Sentiment Analysis of COVID-19 tweets by Deep Learning Classifiers-A study to show how popularity is affecting accuracy in social media.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets