Public Opinions about Online Learning during COVID-19: A Sentiment Analysis Approach
Kaushal Kumar Bhagat, Sanjaya Mishra, Alakh Dixit, Chun‐Yen Chang
Abstract
The aim of this study was to analyze public opinion about online learning during the COVID-19 (Coronavirus Disease 2019) pandemic. A total of 154 articles from online news and blogging websites related to online learning were extracted from Google and DuckDuckGo. The articles were extracted for 45 days, starting from the day the World Health Organization (WHO) declared COVID-19 a worldwide pandemic, 11 March 2020. For this research, we applied the dictionary-based approach of the lexicon-based method to perform sentiment analysis on the articles extracted through web scraping. We calculated the polarity and subjectivity scores of the extracted article using the TextBlob library. The results showed that over 90% of the articles are positive, and the remaining were mildly negative. In general, the blogs were more positive than the newspaper articles; however, the blogs were more opinionated compared to the news articles.