Leveraging IndoBERT and DistilBERT for Indonesian Emotion Classification in E-Commerce Reviews
This work addresses emotion understanding for Indonesian e-commerce customers, but it is incremental as it applies existing models and techniques to a specific language domain.
The study tackled emotion classification in Indonesian e-commerce reviews by leveraging IndoBERT and DistilBERT, achieving 80% accuracy with IndoBERT after data augmentation and hyperparameter tuning.
Understanding emotions in the Indonesian language is essential for improving customer experiences in e-commerce. This study focuses on enhancing the accuracy of emotion classification in Indonesian by leveraging advanced language models, IndoBERT and DistilBERT. A key component of our approach was data processing, specifically data augmentation, which included techniques such as back-translation and synonym replacement. These methods played a significant role in boosting the model's performance. After hyperparameter tuning, IndoBERT achieved an accuracy of 80\%, demonstrating the impact of careful data processing. While combining multiple IndoBERT models led to a slight improvement, it did not significantly enhance performance. Our findings indicate that IndoBERT was the most effective model for emotion classification in Indonesian, with data augmentation proving to be a vital factor in achieving high accuracy. Future research should focus on exploring alternative architectures and strategies to improve generalization for Indonesian NLP tasks.