Sentiment Analysis Applications during COVID-19 Pandemics: An Exploratory Review
Waseem Alromema*, Department of Computers Science and Information System. Taibah University, Al Madinah Almunawara, Saudi Arabia.
Manuscript received on February 28, 2022. | Revised Manuscript received on March 04, 2022. | Manuscript published on March 30, 2022. | PP: 114-118 | Volume-10 Issue-6, March 2022. | Retrieval Number: 100.1/ijrte.F68550310622 | DOI: 10.35940/ijrte.F6855.0310622
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Coronavirus pandemic has created complex challenges and adverse conditions. Sentiment analysis is a process of studying the user application. Because of using the internet in daily activities, many domains and organizations concentrate on analysis or getting user feedback to take the right decision. This paper is review the existing applications that used a sentiments analysis to identify major sentiment trends associated with the push to reopen the analyzing sentiment in social media like Twitter, etc. Data time aligned to the COVID-19 reopening debate. In addition, discover the most popular techniques and approaches. This study focus the research articles in high impact journals that published during the epidemics from 2019 to 2021. The research question that this study answer it are. This study can be beneficial to many domains such as sentiment analysis, text mining, research in related areas, and postgraduate students. This research could present valuable time sensitive opportunities for governments, and the nation into a successful new normal future. Several applications have employed in several domains, including tourism, education, business and health. Health information can be disseminated by social media and misinformation can be addressed via this platform.
Keywords: Sentiment; Data mining; Machine learning; Covid-19; Corona; Pandemic; NLP
Scope of the Article: Data mining