用增强数据提升印尼语电商评论情感分类准确率
Leveraging IndoBERT and DistilBERT for Indonesian Emotion Classification in E-Commerce Reviews
- 用回译和同义词替换增强数据，提升模型表现
- IndoBERT经调参后达80%准确率，优于DistilBERT
- 适合做印尼语NLP、电商情感分析的研究者参考
理解印尼语中的情感对提升电商客户体验至关重要。本研究通过利用先进语言模型IndoBERT和DistilBERT，提升印尼语情绪分类的准确性。关键方法是数据处理，特别是数据增强，包括回译和同义词替换，显著提升了模型性能。经过超参数调优，IndoBERT达到80%的准确率，表明精细数据处理的重要性。虽多模型融合略有提升，但未显著改善效果。结果表明，IndoBERT在印尼语情绪分类中最为有效，数据增强是实现高准确率的关键因素。未来研究应探索其他架构与策略，以提升印尼语NLP任务的泛化能力。
原文摘要 · Abstract (English)
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.
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