Two recent papers benchmark traditional machine learning models against deep learning approaches for sentiment analysis on Indonesian text data. One study on Tokopedia reviews found that a Linear SVC model outperformed IndoBERT, achieving 97.60% accuracy, attributed to differences in data sampling. Another paper analyzing Spotify reviews indicated that while BiLSTM achieved a higher overall weighted F1-score, traditional ML methods with SMOTE provided more balanced three-class performance. AI
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IMPACT Highlights that traditional ML models can still outperform advanced deep learning on specific NLP tasks, especially with imbalanced datasets.
RANK_REASON Two academic papers published on arXiv compare traditional ML models with deep learning for sentiment analysis tasks.