Comparative Study of Naïve Bayes Classifier and Support Vector Machine Methods in Public Sentiment Analysis of Prabowo-Gibran's Free Lunch Program

View Full Paper

Ask AI

Bookmark

Authors

Discussion

Analysis compares sentiment classification accuracy of naive bayes and support vector machine using social media data on a public program.

Naive Bayes classifier achieved an accuracy of 84.38%, performing significantly better than support vector machine.
With 156 preprocessed data points, sentiment analysis revealed 127 negative and 31 positive sentiments.
Assessment followed the CRISP-DM framework using Python and Scikit-learn for model training and evaluation.
Results suggest Naive Bayes provides better balance in sentiment classification and overall performance.

Febrian et al. (2025) studied this question.

View Full Paper

Ask AI

Bookmark

Analysis compares sentiment classification accuracy of naive bayes and support vector machine using social media data on a public program.