Abstract
Social media is a valuable source of information that allows to study people opinions of many events that happen every day. Nowadays social networks are one of the most important communication methods that people use. Feelings towards nations can be measured today thanks to the advances in machine learning and big data. In this paper we present a method to identify if there are negative sentiments towards China as a result of the COVID-19 virus. The method is based on sentiment analysis and extracts information from the Twitter social network. This analysis was done with the VADER library, a rule-based tool that provides classification algorithms. A dataset of 30,000 tweets was built for three time windows: December 2019 (before the pandemic), March 2020 (month in which the pandemic was confirmed by the World Health Organization), and May 2020 (when some countries started the de-escalation phase). Results show that sentiments became negative towards China and social network data allows to confirm this situation.
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Muñoz, L.M., Ramirez, M.F., Camargo, J.E. (2020). A Data-Driven Method for Measuring the Negative Impact of Sentiment Towards China in the Context of COVID-19. In: Florez, H., Misra, S. (eds) Applied Informatics. ICAI 2020. Communications in Computer and Information Science, vol 1277. Springer, Cham. https://doi.org/10.1007/978-3-030-61702-8_15
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