COVID-19 derived from SARS-CoV-2 is currently spreading dramatically worldwide and causing millions of infections and deaths amongst the human population (Liu et al., 2020). SARS-CoV-2 was detected in China in late 2019 in a general seafood marketplace and eventually infected millions of people (Velavan & Meyer, 2020). The situational reports of the World Health Organisation’s (WHO) statistics have indicated that the number of confirmed cases exceeds 37,109,851, and the number of deaths exceeds 1,070,355 worldwide (WHO, 2020). Accurate insights into COVID-19 can only be obtained when the pandemic ends as literature and statistics are proliferating, and keeping data updated is nearly impossible (Hamzah et al., 2020). On 28 February 2020, the WHO launched emergency protocols in all medical and public health systems because of the severity and risks of COVID-19 (Epidemiol, 2020). COVID-19 is not the first global pandemic. Several different viruses and pandemics, including Ebola (McMullan, 2020), Mers-Cov and SARS, have occurred in the past. 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). Current and genuinely reasonable sources for obtaining these data include social media platforms that provide available data more than ever before. Interestingly, these data serve as the basis for conducting opinion mining and sentiment analysis. Social media has launched platforms to facilitate communications amongst human communities and help them share ideas, information, knowledge and other data by these forms of electronic communication (K.-S. Kim, Sin, & Yoo-Lee, 2014). Social media platforms are gaining remarkable influence than ever before and are considered one of the fastest growing information systems for social applications (Appel, Grewal, Hadi, & Stephen, 2020; Xu, Zayed, Lin, Wang, & Li, 2020). Social media platforms are considered the global centre of big data as people use their applications and spend excessive hours on these media outlets (DeNardis & Hackl, 2015). Some of the most commonly employed social media applications in the world are Facebook, Twitter, Instagram and Reddit (K. Ali, Dong, Bouguettaya, Erradi, & Hadjidj, 2017). Social and statistical studies have shown that these applications influence human behaviours, given the users’ length of time spent on them, which ranges from hours per week to daily use (Statista, 2019). Despite the large data presented on these social media platforms, their content may have contradictory effects, which range from negative psychological influence on people’s lives to positive psychological influence on people’s lives (Crawford, 2009). People who are addicted to social media likely unleash and share opinions and ideas across these platforms (Jansen, Sobel, & Cook, 2010). Subsequently, turning these opinions and posts into assets is highly valuable. Discovering a Tweet or Facebook post may be possible with millions of likes and retweets, but this massive interaction with such a post does not reflect its importance or the emotions of users who participate in the post because of many factors, such as the nature of posts, including negation and irony (Ji, Chun, Wei, Geller, & Mining, 2015); happiness and sadness (K. Ali et al., 2017); anger (Ji et al., 2015); positive and negative (Zarrad, Jaloud, & Alsmadi, 2014); concern, surprise, disgust or confusion (Ji, Chun, & Geller, 2016); and the massive numbers of tweets (Gayo-Avello et al., 2013). Nevertheless, large-scale extractions of human emotions and entertainment from social media networks are essential for international public influences, business decisions and policy development. Sentiment analysis and opinion mining have become useful. Sentiment analysis contributes to the understanding of human emotions as it can seek people’s behaviours as users engage in these social media applications (Ji et al., 2016). Additionally, these media applications have been employed in various application domains, including tourism (Ainin, Feizollah, Anuar, & Abdullah, 2020), business (Reyes-Menendez, Saura, & Filipe, 2020), education (Hassan et al., 2020) and health (Rodrigues, das Dores, Camilo-Junior, & Rosa, 2016), for various beneficial purposes, such as analysing opinions (Zarrad et al., 2014) and allowing people to express their emotions freely (Chung et al., 2015), and for highly dynamic and real-time data trends (Chaudhary & Naaz, 2017). With this feature, large-scale communities can be observed at a low cost (Choi et al., 2017). Therefore, sentiment analysis is a powerful tool in understanding the most important events and trends.<br /><br /> Dr. Rami Qays<br /><br />