Sentiment Classification and Interpretation of Tokopedia Reviews: A Machine Learning, IndoBERT, and LIME Approach - Mbake Woka, Adrian Yoris; Purbolaksono, Mahendra Dwifebri; Utama, Dody Qori (Building of Informatics, Technology and Science (BITS), 2025)
Sentiment classification of user reviews plays a vital role in business decision-making, especially on e-commerce platforms like Tokopedia. This study evaluates the performance of various sentiment classification models such as Logistic Regression LinearSVC, and BERT models, both baseline and fine-tuned. Evaluation metrics used include accuracy, precision, recall, and F1-score, applied to Tokopedia review data labelled based on user ratings. The result is fine-tuned BERT model has the best and consistent result, with 92% accuracy and 0.92 f1-score for each class. This shows that fine-tuned BERT can effectively capture the semantic context of user reviews. Its consistent performance across classes makes it suitable for reliable sentiment classification in real-world applications. Furthermore, fine-tune BERT model is visualized by Local Interpretable Model-agnostic Explanation to identify features – in this case is word – that indicates sentiment as positive or negative. It will show as color, orange for positive and blue as negative. This method will make the model more transparent and more reliable.