Sentiment analysis of social media response on the Covid19 outbreak

Sentiment analysis and its applications in fighting COVID-19 and infectious diseases: A systematic review

“…Decision makers’ challenges include identifying people’s sentiments on a subject and their beliefs ( Zarrad, et al, 2014 ) towards public health policy decision-making ( Chung, et al, 2015 ; S. Li et al, 2020 , Lwin et al, 2020 ) in extremely serious cases, such as disease outbreaks ( Chung, et al, 2015 ). In addition, this dilemma will challenge decision makers to take serious measures without delay ( Ji, et al, 2015 ) and control the spread of an event or even quarantine confirmed cases of infectious diseases ( Bhat et al, 2020 , Choi et al, 2017 , Ji et al, 2013 , Singh et al, 2018 ). Aside from decision makers, the scientific community has an equally important role in fighting these outbreaks via sentiment analysis; however, fully utilising the abilities of the community remains elusive because of a lack of scientific reference work ( Chung, et al, 2015 ), the availability of studies in certain languages only ( Baker, et al, 2020 ) or the absence of relevant research with the latest trends ( Ji, et al, 2015 ).…”

“… Kim, et al (2016) TwitterNews Publications 16,189 news articles7,106,297 tweets Between 1 June 2014 and 31 August 2014 Ebola Sentiment Dynamics of the Hot Health Issue of Ebola Lexicon Based Models Chung, et al (2015) Twitter 255,118 tweets January 2015 Ebola Ebola Outbreak Discussions on Twitter Lexicon Based Models Deng, et al (2015) SinaTencentSohuNeteaseTwitter 140,000 tweets180,000 records June to November 2014 EbolaTyphoon HaiyanHagupit Distributed Mining System for Online Opinion Data Collecting and Mining. Lexicon Based Models Zarrad, et al (2014) Twitter 1,500,000 tweets 3 months MERS-CoV Addressing New Challenges for Big Data Platform with an Opinion Mining Approach Lexicon Based Models K. Ali, et al (2017) TwitterRedditInstagramNews Fora 525 Reviews 2 Months Outbreaks Locations Monitoring for Disease Outbreak Lexicon Based Models Almazidy, Althani, and Mohammed (2016) Twitter N/A N/A Outbreak Outbreak Notification Framework Using Twitter Mining for Smart Home Dashboards Lexicon Based Models Bhat, et al (2020) Twitter N/A N/A COVID-19 Sentiment analysis of Social Media Response on the COVID-19 Outbreak Lexicon Based Models Lwin, et al (2020) Twitter 20,325,929 tweets 28 January 2020 to 9 April 2020 COVID-19 Examining Worldwide Trends of Different Emotions During the COVID-19 Pandemic. Lexicon Based Models Raamkumar, Tan, and Wee (2020) Facebook 3,185,460 posts 1 January 2019 to 18 March 2020 COVID-...…”

“…This approach was recommended and considered an effective method for human-computer interaction ( HCI ). In the following work by Bhat, et al (2020) , more than 80,000 tweets associated with the spread of COVID-19 were subjected to sentiment analysis. In particular, 51.97%, 34.05% and 13.96% of the tweets expressed positive sentiments, neutral sentiments and negative sentiments, respectively.…”

“…Thus, we obtained research in Arabic, as this case started in this region and attracted many researchers to work with the language of this domain. However, COVID-19 has been extremely widespread compared with dreaded epidemics and pandemics, such as ‘The Great Influenza’ (Spanish flu of 1918) or the Black Death (form of bubonic plague), which spread worldwide ( Bhat, et al, 2020 ). During these times, no computerised technologies or analytical methods, such as sentiment analysis, were available to study these phenomena.…”

“…Medical doctors and medical researchers have earnestly dealt with these pandemics, and their efforts have not been in vain ( Elder, Johnston, Wallis, & Crilly, 2020 ). Nevertheless, with the current trends of technologies, especially the role of computer science, computer technologies have fairly shown their contribution to medical decisions, such as infectious diseases and outbreaks ( Bhat, et al, 2020 ; Soliman, Tabak, & Sciences, 2020). Historical data are utilised in the process, and an increase in the availability of data enables researchers to generate better decisions and conclusions ( Pan, et al, 2020 ).…”