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Comparison of Support Vector Machine and Naïve Bayes to Sentiment Analysis of Military Barracks Program

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Sentiment analysis reveals support vector machine outperforms naive bayes in assessing public opinion on military barracks program.

The support vector machine achieved higher accuracy than naive bayes in predicting sentiment.
Overall, SVM reported 1429 positive tweets and 447 negative tweets, outperforming naive bayes's 1309 positive and 567 negative.
Public sentiment analysis showed that the majority view the military barracks program positively, indicating general support.
Data preprocessing techniques included normalization, stopword removal, and tokenization to prepare tweets for analysis.

Nurzanah et al. (2025) studied this question.

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