arXiv 2605.013222026-05-02cs.CL

对印尼电子商务评论进行情感分析的LightGBM与BiLSTM基准测试

Benchmarking LightGBM and BiLSTM for Sentiment Analysis on Indonesian E-Commerce Reviews

Department of Data Science（数据科学系）；Institut Teknologi Sumatera（苏门答腊理工大学）

Lidia Natasyah Marpaung, Vania Claresta, Iqfina Haula Halika, Luluk Muthoharoh, Ardika Satria, Martin Clinton Tosima Manullang

备注：: 9 pages, 3 figures, 2 tables. The paper compares LightGBM, Logistic Regression, and linear SVM against a BiLSTM model for Indonesian e-commerce sentiment analysis using a 15,000-sample dataset from Hugging Face

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AI总结：

本文比较了自然语言处理中机器学习和深度学习方法在印尼电商评论情感分析中的表现，发现BiLSTM在准确率和F1分数上优于所有机器学习模型，而LightGBM在训练效率上表现最佳。

摘要：

本研究比较了自然语言处理中两种主要方法：机器学习（使用PyCaret AutoML框架）和深度学习。评估基于来自Hugging Face的印尼电商评论数据集，包含15,000个样本，分为训练、验证和测试集。机器学习实验比较了LightGBM、逻辑回归和支持向量机（SVM）算法，深度学习实验实现了双向长短期记忆（BiLSTM）架构。实验结果表明，BiLSTM模型在准确率和F1分数上均优于所有机器学习模型，达到98.87%。同时，LightGBM在训练效率上表现最佳，准确率为98.23%。本研究证明BiLSTM架构能够有效捕捉印尼评论文本的序列上下文，使其成为该特定分类任务的最优模型。

英文摘要：

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.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。