Sentiment Analysis of Face-to-face Learning during Covid-19 Pandemic using Twitter Data

Abstract

Covid-19 pandemic has massive impacts on the activity of human in the world, including in Indonesia. To reduce the transmission of the virus, Indonesian government issues a policy to restrict daily public activities, affecting key national sectors, such as education systems. All learning activities are switched from the conventional face-to-face mode to being remote via the use of the Internet. After the pandemic begins to subside, the government then plans to reopen all schools and to allow face-to-face learning. However, this decision has sparked controversy in the social media, including Twitter. This paper describes a methodology to perform sentiment analysis on a collection of tweets that are in connection with the restart of the face-to-face learning mode. In particular, our experiments using hand-crafted features based on the tweets demonstrate that data-driven models are useful for automatic sentiment orientation classification on Twitter data. The best model achieved in this study has 69,1% accuracy, 68.6% precision, 69.1% recall, and 67,8% F1-Score. This result is achieved by using unigram, Support Vector Machine, and tweet + number of words (count) feature combinations.