<![CDATA[The rapid growth of the beauty industry has generated a large volume of consumer reviews, necessitating an automated processing system to understand public sentiment. This study aims to implement sentiment analysis on skincare product reviews using the Multinomial Naïve Bayes algorithm. The labeling process was conducted by converting star ratings into sentiment categories: ratings 4 and 5 were labeled as positive, ratings 1 and 2 as negative, while rating 3 was excluded to avoid data ambiguity. The feature representation stage utilized TF-IDF with an N-gram approach (unigram and bigram), generating 10,000 features from a dataset of 8,646 reviews. Based on the testing results of 1,730 test data, the model achieved an accuracy of 70%. The Confusion Matrix evaluation revealed that the model performed exceptionally well in the positive class, reaching a recall of 1.00. However, the model struggled to classify negative and neutral classes, with recall values approaching 0.00. This was caused by imbalanced data distribution, where positive reviews significantly dominated the dataset. Nevertheless, Multinomial Naïve Bayes proved efficient in handling large-scale frequency-based textual features. A weighted average F1-score of 0.58 suggests that dataset optimization is required to improve the model's ability to accurately recognize minority sentiments.]]>
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