Sentiment Analysis of Indonesian Spotify Reviews Using Machine Learning and BiLSTM
For researchers working on Indonesian sentiment analysis, this is an incremental benchmark study comparing standard methods on a new dataset.
This paper benchmarks machine learning and BiLSTM models for three-class sentiment classification of Indonesian Spotify reviews, finding that BiLSTM achieves the highest weighted F1-score but fails on the minority neutral class, while Decision Tree with SMOTE provides more balanced performance.
This paper benchmarks classical machine learning and deep learning approaches for three-class sentiment classification of Indonesian Spotify reviews. Using 100,000 scraped reviews and 70,155 cleaned samples, the study compares Support Vector Machine, Multinomial Naive Bayes, and Decision Tree models with a two-layer BiLSTM. Both approaches use the same preprocessing pipeline, including slang normalization, stopword removal, and stemming. Decision Tree achieves the best performance among the classical models, while BiLSTM attains the highest weighted F1-score overall but fails on the minority neutral class. The paper concludes that BiLSTM is stronger for overall sentiment detection, whereas machine learning with SMOTE provides more balanced three-class performance.