A multi-task BiLSTM with shared encoder and AutoML pipeline classifies sentiment and emotions on the PRDECT-ID Indonesian review dataset with public code and Gradio demos.
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DistilBERT achieves 84.78% accuracy and 84.75% F1-score on binary sentiment classification of Indonesian student opinions about AI in higher education, outperforming SVM at 82.14%.
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Sentiment and Emotion Classification of Indonesian E-Commerce Reviews via Multi-Task BiLSTM and AutoML Benchmarking
A multi-task BiLSTM with shared encoder and AutoML pipeline classifies sentiment and emotions on the PRDECT-ID Indonesian review dataset with public code and Gradio demos.
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Sentiment Analysis of AI Adoption in Indonesian Higher Education Using Machine Learning and Transformer-Based Models
DistilBERT achieves 84.78% accuracy and 84.75% F1-score on binary sentiment classification of Indonesian student opinions about AI in higher education, outperforming SVM at 82.14%.