Analysis of public sentiment about COVID-19 in Indonesia on Twitter using multinomial naive bayes and support vector machine

Abstract

The Covid-19 case which initially occurred in China until now has spread to all continents, including Indonesia. Indonesia itself was first affected by the Covid-19 virus in February 2020 which made a crowd in cyberspace including in particular Twitter. Everyone is free to give their opini or opinions about Covid-19 so that it raises many opinions, not only positive or neutral opinions but also negative opinions regarding Covid-19. Social media is now not only used as a means of making friends or making friends, but also used for other activities. The purpose of this research is to build a sentiment analysis method with the theme of the Covid-19 Pandemic on Twitter social media with the most optimal and maximum accuracy. Meanwhile, the benefit is to help Indonesians conduct research on public opinion on Twitter which contains positive, neutral, or negative sentiments. In this study, Text Mining has been used using the Support Vector Machine and Multinomial Naive Bayes method, which is one of the methods used to conduct sentiment analysis. The results of the Support Vector Machine and Naive Bayes Multinomial method used for sentiment analysis are useful for obtaining information and knowledge about Covid-19. The results obtained are that the opinion of the Indonesian people regarding Covid-19 is that the Positive and Neutral amount is more than the Negative with an average of 40% Positive and Neutral while the Negative is 20%. For the calculation of the F-1 average value, it can be seen that the Support Vector Machine method is the best model in this study with a value of 93%. little difference with the Naive Bayes Multinomial method of 92%. It is hoped that this research will continue with more data and use other sentiment analysis methods in order to find out the average results of the opinions of the Indonesian people regarding Covid-19. Keywords - Sentiment Analysis, Multinomial Naive Bayes, Twitter, Data Mining, Covid-19.