Sentiment Analysis of Public Opinion on the 2024 President-Elect's Administration on Twitter using Naïve Bayes

Abstract

The increased use of social media, especially Twitter, has created a need for systematic analysis to understand public opinion on political issues, including the performance of the president-elect in 2024. This study analyzes public opinion on these issues using the Naïve Bayes algorithm. Data was collected using scraping techniques and then divided into three sentiment categories positive, negative, and neutral. After the labeling process, the data underwent preprocessing, which included data cleaning, case folding, normalization, tokenization, stop word removal, and stemming. TF-IDF weighting was used to represent features, while the SMOTE technique was applied to balance class distribution. A total of 1,074 tweets were analyzed. The results showed that negative opinions dominated at 59.9%, followed by positive opinions at 29.8% and neutral opinions at 10.3%. Model performance evaluation showed that Naïve Bayes was able to consistently identify sentiment patterns, with 71% accuracy, 74% precision, 71% recall, and an F1 score of 72%. These results prove that the combination of TF-IDF and SMOTE contributes significantly to improving classification effectiveness. This study provides a comprehensive overview of trends in public opinion.