Leveraging IndoBERT and DistilBERT for Indonesian Emotion Classification in E-Commerce Reviews
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arXiv
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| Hauptverfasser: | , , , |
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| Format: | Preprint |
| Veröffentlicht: |
2025
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| _version_ | 1866914044585181184 |
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| author | Christian, William Adamlu, Daniel Yu, Adrian Suhartono, Derwin |
| author_facet | Christian, William Adamlu, Daniel Yu, Adrian Suhartono, Derwin |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_14611 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Leveraging IndoBERT and DistilBERT for Indonesian Emotion Classification in E-Commerce Reviews Christian, William Adamlu, Daniel Yu, Adrian Suhartono, Derwin Computation and Language 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. |
| title | Leveraging IndoBERT and DistilBERT for Indonesian Emotion Classification in E-Commerce Reviews |
| topic | Computation and Language |
| url | https://arxiv.org/abs/2509.14611 |