Sentiment Analysis of Potential Presidential Candidates 2024: A Twitter-Based Study

Abstract

This study aims to analyze the sentiment towards potential presidential candidates for the 2024 election in Indonesia based on Twitter users' opinions. Three prominent figures, Ganjar Pranowo, Anies Baswedan, and Prabowo Subianto, were surveyed to gauge their electability. Using machine learning classification methods, Support Vector Machine, Bernoulli Naïve Bayes, and Logistic Regression, sentiment classification was performed. The findings indicate that Twitter users expressed predominantly positive sentiments towards each potential candidate. The evaluation of the classification algorithms showed SVM with 84% accuracy, Bernoulli Naïve Bayes with 77%, and Logistic Regression with 84%. This research sheds light on public sentiment towards potential leaders, offering valuable insights for political strategists and decision-makers in shaping effective election campaigns. Highlight: Sentiment Analysis: The study employs machine learning techniques to analyze the sentiments expressed by Twitter users towards potential presidential candidates for the 2024 election in Indonesia. Positive Sentiments: The findings reveal that Twitter users predominantly exhibit positive sentiments towards all three potential candidates, Ganjar Pranowo, Anies Baswedan, and Prabowo Subianto. Election Insights: This research provides valuable insights into public sentiment, offering valuable information for political strategists and decision-makers in devising effective election campaigns for the upcoming presidential election. Keyword: Sentiment Analysis, Twitter Users, Potential Presidential Candidates, Machine Learning, Election 2024