Sentiment Analysis of Public Reaction to COVID19 in Twitter Media using Naïve Bayes Classifier

Iksan, N.; Widodo, D. A.; Sunarko, B.; Udayanti, E. D.; Kartikadharma, E.

Currently, the world's attention was focused on the disease outbreak, namely the corona virus (COVID19). World Health Organization (WHO) declare that this virus was a global pandemic in all countries. The various impacts that arise due to this virus cover various fields, namely health, social, political, religious, economic to resilience and security. Some of the services currently used were still focused on the health sector, namely in the form of treatment and information services related to the development of the spread of the virus. This research will develop a service that was used to identify social impacts in the community through observing community activities on social media, namely Twitter, in the form of an analysis of the public's reaction to COVID19. Through this Twitter, a data acquisition process will be carried out to obtain data related to COVID19 which will then be carried out a sentiment analysis using the Naïve Bayes method so that the results of the public reaction sentiment will be obtained. The experimental result shows that prediction accuracy was 0,86. Furthermore, the results of the Recall was 0,687, the precision was 0,827 and the F-Score was 0.749 © 2021 IEEE.