Comparison of Multinomial Naïve Bayes and Bernoulli Naïve Bayes Methods in Sentiment Analysis of National Team Naturalization Based on Comments on Instagram
Keywords:
sentiment analysis, instagram, multinomial naïve bayes, bernoulli naïve bayes, naturalization of foreign playersAbstract
Football is the most popular sport in Indonesia, with the National Team serving as a symbol of national pride. One of the most debated issues is the naturalization of foreign players to strengthen the team. Public opinions on this topic are widely expressed on social media, especially Instagram. This study aims to analyze sentiment from 4,258 Instagram comments related to the naturalization issue by comparing the performance of Multinomial Naïve Bayes and Bernoulli Naïve Bayes Methods. The analysis process includes preprocessing, translation using the Google Translate API, sentiment labeling with VADER, TF-IDF vectorization, and data balancing using SMOTE. The classification performance is evaluated using a confusion matrix with metrics such as accuracy, precision, recall, and F1-score. The results show that Multinomial Naïve Bayes achieves better performance with 74.65% accuracy, 75.96% precision, 74.65% recall, and 74.32% F1-score under 10-fold cross-validation and alpha 0.01 compared to Bernoulli Naïve Bayes.
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