Analisis Sentimen Pengguna Media Social X terhadap Vaksinasi Covid 19 Menggunakan Lexicon Based dan Naive Bayes

Abstract

Facing the COVID-19 pandemic, understanding public sentiment towards COVID-19 vaccination through social media, particularly Social Media X, is crucial. This research aims to analyze Social Media users' sentiments regarding COVID-19 vaccination in Indonesia using Lexicon-Based and Naive Bayes methods. COVID-19 vaccine-related tweets were collected from all provinces in Indonesia, totaling 2,879 tweets. The dataset was divided into 2,164 training tweets and 542 testing tweets, with an 80% to 20% ratio. Keywords focused on are sentiment analysis, COVID-19 vaccination, Social Media X , Lexicon-Based method, Naive Bayes, and Indonesia. The Lexicon-Based method was utilized to classify sentiment in each tweet based on provided positive-negative dictionaries, while Naive Bayes was employed for supervised classification analysis. The objective i of this research is not only i to understand public perspectives but also to enhance the accuracy of previous sentiment analysis results. It is hoped that this study will i provide deeper insights into public views on COVID-19 vaccination in Indonesia, which can be utilized to improve communication strategies and disease prevention efforts.