对比轻量模型与深度学习在印尼电商评论情感分析中的表现
Benchmarking LightGBM and BiLSTM for Sentiment Analysis on Indonesian E-Commerce Reviews
- 用LightGBM和BiLSTM分别处理文本，比较性能差异
- BiLSTM准确率98.87%，优于所有机器学习模型
- 适合关注中文/东南亚语种情感分析的研究者
本研究对比了自然语言处理中的两种主要方法：基于PyCaret AutoML框架的机器学习（ML）与深度学习（DL）。实验使用来自Hugging Face的15,000条印尼电商评论数据集，按训练、验证和测试集划分。机器学习部分比较了LightGBM、逻辑回归和支持向量机（SVM），深度学习部分采用双向长短期记忆网络（BiLSTM）结构。结果显示，BiLSTM模型在所有模型中表现最佳，准确率达98.87%，F1分数也为98.87%；而LightGBM作为最优的机器学习模型，准确率为98.23%，且训练时间极快。研究证明，BiLSTM能有效捕捉印尼语评论的序列上下文信息，是该分类任务的更优选择。
原文摘要 · Abstract (English)
This study presents a comparative analysis between two primary approaches in Natural Language Processing (NLP): Machine Learning (ML) utilizing the PyCaret AutoML framework, and Deep Learning (DL). The evaluation is conducted on a sentiment analysis task using an Indonesian e-commerce review dataset sourced from Hugging Face. The dataset, consisting of 15,000 samples, is partitioned into training, validation, and testing sets. The ML experiments compare LightGBM, Logistic Regression, and Support Vector Machine (SVM) algorithms, whereas the DL experiment implements a Bidirectional Long Short-Term Memory (BiLSTM) architecture. The experimental results demonstrate that the BiLSTM model outperforms all ML models, achieving an accuracy of 98.87\% and an F1-Score of 98.87\%. Meanwhile, LightGBM emerges as the best-performing ML model with an accuracy of 98.23\% in a highly efficient training time. This research proves that the BiLSTM architecture is highly capable of capturing the sequential context of Indonesian review texts, making it the superior model for this specific classification task.
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