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This is a Quantitative Study study.

Sentiment Analysis of Twitter Data on Indonesia’s Cabinet Using Naïve Bayes and Support Vector Machine Algorithms

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This analysis compares sentiment classification using Naïve Bayes and SVM algorithms, highlighting their effectiveness in evaluating public opinions.

The SVM algorithm achieved 96.77% accuracy compared to 91.70% for the Naïve Bayes Classifier algorithm.
SVM outperformed Naïve Bayes in classifying tweets, but both were effective for sentiment analysis.
Naïve Bayes tends to misclassify neutral tweets as positive, while SVM struggles with sarcasm and subtle sentiments.
The study utilized a dataset from Twitter focusing on public opinions about Jokowi's ministers.

Riyantoro et al. (2025) studied this question.

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This analysis compares sentiment classification using Naïve Bayes and SVM algorithms, highlighting their effectiveness in evaluating public opinions.