BiLSTM achieves 98.87% accuracy and F1-score on Indonesian e-commerce review sentiment analysis, slightly outperforming the best machine learning model LightGBM at 98.23%.
Sentiment analysis of twitter data related to rinca island development using doc2vec and svm and logistic regression as classifier
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Benchmarking LightGBM and BiLSTM for Sentiment Analysis on Indonesian E-Commerce Reviews
BiLSTM achieves 98.87% accuracy and F1-score on Indonesian e-commerce review sentiment analysis, slightly outperforming the best machine learning model LightGBM at 98.23%.