Effects of the COVID 19 Pandemic on Classrooms: A Case Study on Foreigners in South Korea Using Applied Machine Learning
In this study, we qualitatively and quantitatively examine the effects of COVID 19 on classrooms, students, and educators Using a new Twitter dataset specific to South Korea during the pandemic, we sample the sentiment and strain on students and educators using applied machine learning techniques in order to identify various topical pain points emerging during the pandemic Our contributions include a novel and open source geo fenced dataset on student and educator opinion within South Korea that we are making available to other researchers as well We also identify trends in sentiment and polarity over the pandemic timeline, as well as key drivers behind the sentiments Moreover, we provide a comparative analysis of two widely used pre trained sentiment analysis approaches with TextBlob and VADER using statistical significance tests Ultimately, we analyze how public opinion shifted on the pandemic in terms of positive sentiments about accessing course materials, online support communities, access to classes, and creativity, to negative sentiments about mental fatigue, job loss, student concerns, and overwhelmed institutions We also initiate initial discussions about the concept of actionable sentiment analysis by overlapping polarity with the concept of trigger management to assist users in coping with negative emotions We hope that insights from this preliminary study can promote further utilization of social media datasets to evaluate government messaging, population sentiment, and multi dimensional analysis of pandemics