NAÏVE BAYES ALGORITHM OPTIMIZATION USING PARTICLE SWARM OPTIMIZATION (PSO) FOR COVID-19 VACCINE SENTIMENT ANALYSIS ON TWITTER

Abstract

The Covid-19 vaccine is a vaccine that is quite popular, because it is the most needed and most discussed vaccine. There are 5 types of vaccines that are very popular including AstraZeneca, Moderna, Pfizer, Sinopharm and Sinovac. Sentiment analysis is a branch of text classification with computational linguistics and natural language processing that refers to a broad field, and text mining has a function to analyze opinions, judgments, sentiments, attitudes, evaluations and emotions of a person regarding an individual, organization, certain topics, services and other activities. This study aims to classify public sentiment towards the type of Covid-19 vaccine on social media Twitter, whether the opinion is positive or negative by using the Naïve Bayes algorithm based on Particle Swarm Optimization (PSO). The conclusion of this study is that the results of testing the Naïve Bayes algorithm with PSO using RapidMiner software are 79.17% accuracy, 87.69% precision, 85.07% recall for AstraZeneca vaccine, 68.82% accuracy, 92.29% precision, 71.72% recall for Moderna vaccine, 67.54% accuracy, precision 77.83%, recall 62.95% for Pfizer vaccine, accuracy 93.33%, precision 91.67%, recall 100.00% for Sinopharm vaccine, and accuracy 74.93%, precision 82.61%, recall 70.90% for Sinovac vaccine. It can be concluded that with the help of optimization PSO, the resulting confusion matrix value is greater and is proven to be more accurate.

Keywords : Vaccine; Covid-19; Sentiment Analysis; Naive Bayes; Particle Swarm Optimization.