Sentiment Analysis on the Impelementation of the 2024 Presidential Election on X Application Using Naive Bayes and Support Vector Machine (SVM) Methods

Abstract

This article discusses sentiment analysis of the 2024 Presidential Election through data collected from platform X, this research aims to identify and classify positive, negative, and neutral sentiments in X related to the election. This research uses scoring and textblob methods. Where the scoring method is the process of deeply analyzing sentiment and then giving the sentiment value of each data comment and accumulating it. While the textblob method is a tool used specifically for text processing that can provide sentiment for each comment. The results of the analysis provide in-depth understanding of the responses and views of the community manifested in digital space. Sentiment analysis can serve as a guide to understanding the dynamics of public opinion during the election process and its potential to influence public participation and trust in the democratic system. The practical implications of this research include the development of more effective political communication strategies based on an understanding of the sentiments that develop on X.