<![CDATA[The rapid growth of e-commerce has increased the volume of customer reviews, making it difficult for fashion businesses to manually identify customer sentiment, product concerns, and satisfaction drivers. This study aims to evaluate the performance of Naive Bayes, Support Vector Machine (SVM), Long Short-Term Memory (LSTM), and IndoBERT in classifying sentiment polarity in Indonesian fashion product reviews, while also identifying topic-level sentiment patterns using Latent Dirichlet Allocation (LDA). The dataset consisted of 23,487 Shopee Indonesia fashion reviews collected during the 2025 observation period. After data cleaning, duplicate removal, text normalization, tokenization, stopword removal, and lemmatization, 22,961 valid reviews were retained and categorized into positive, neutral, and negative classes using rating-based labeling. To reduce majority-class bias, stratified data splitting and class-weighted learning were applied, while five-fold cross-validation was used to evaluate model stability. The results show that IndoBERT achieved the highest performance with an accuracy of 93.41%, precision of 92.87%, recall of 92.15%, and F1-score of 92.51%, outperforming LSTM with 90.80% accuracy, SVM with 88.90%, and Naive Bayes with 84.60%. The findings indicate that transformer-based contextual representation is more effective in handling noisy Indonesian fashion review text, including informal expressions, abbreviations, and context-dependent sentiment. Topic-based analysis further revealed that positive sentiment was mainly associated with product quality, material comfort, design, and value, while negative sentiment was driven by sizing mismatch, product inconsistency, delivery delay, and customer service issues. This study contributes to sentiment classification research by providing a comparative evaluation of machine learning, deep learning, and transformer-based models, and offers practical insights for improving fashion e-commerce product communication, customer satisfaction, and data-driven digital marketing decisions.]]>
