arXiv 2604.254522026-04-28cs.CL

在印尼产品评论上基于BiLSTM与注意力机制对逻辑回归、SVM和LightGBM的基准测试

Benchmarking Logistic Regression, SVM, and LightGBM Against BiLSTM with Attention for Sentiment Analysis on Indonesian Product Reviews

Institut Teknologi Sumatera（印度尼西亚苏门答腊理工大学）

Razin Hafid Hamdi, Ivana Margareth Hutabarat, Hanna Gresia Sinaga, Luluk Muthoharoh, Ardika Satria, Martin C. T. Manullang

备注：: 6 pages, 2 figures. Benchmarking study comparing PyCaret-based machine learning models (Logistic Regression, SVM, LightGBM) with a BiLSTM+Attention model for sentiment analysis on Indonesian product reviews

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AI总结：

本文比较了逻辑回归、SVM和LightGBM与基于BiLSTM和注意力机制的深度学习方法，在印尼产品评论二分类中的性能。传统机器学习方法在高维数据集上可与复杂深度学习架构竞争并略微领先，同时计算效率更高。

摘要：

电子商务平台上的产品评论情感分析在自动理解客户满意度和为卖家提供改进产品质量的行动见解方面起着关键作用。本文提出了一项全面的基准研究，比较了通过PyCaret AutoML框架实现的机器学习（ML）方法与基于双向长短期记忆（BiLSTM）架构和注意力机制的深度学习（DL）方法，用于印尼产品评论的二元情感分类。数据集包含19728个样本，正负评论平衡。对于ML方法，通过10折分层交叉验证评估了三种主要算法：逻辑回归（LR）、具有线性内核的支持向量机（SVM）和Light梯度提升机（LightGBM）。逻辑回归在ML方法中表现最佳，准确率为97.26%，F1分数为97.26%。BiLSTM与注意力机制模型在3946个保留测试样本上实现了97.24%的准确率和97.24%的F1分数。这些比较结果表明，传统机器学习算法在适当预处理和特征提取的情况下，可以与更复杂的顺序DL架构竞争，甚至略微领先，同时同时提供更高的计算效率。

英文摘要：

Sentiment analysis of product reviews on e-commerce platforms plays a critical role in automatically understanding customer satisfaction and providing actionable insights for sellers seeking to improve product quality. This paper presents a comprehensive benchmarking study comparing a Machine Learning (ML) approach via the PyCaret AutoML framework against a Deep Learning (DL) approach based on a Bidirectional Long Short-Term Memory (BiLSTM) architecture with an Attention mechanism for binary sentiment classification on Indonesian product reviews. The dataset comprises 19,728 samples balanced equally between positive and negative reviews. For the ML approach, three prominent algorithms were evaluated via 10-fold stratified cross-validation: Logistic Regression (LR), Support Vector Machine (SVM) with a linear kernel, and Light Gradient Boosting Machine (LightGBM). Logistic Regression achieved the best ML performance with an accuracy of 97.26\% and an F1-score of 97.26\%. The BiLSTM with Attention model, evaluated on 3,946 held-out test samples, achieved an accuracy of 97.24\% and an F1-score of 97.24\%. These comparative results demonstrate that traditional ML algorithms with proper preprocessing and feature extraction can compete closely with, and even marginally outperform, more complex sequential DL architectures on high-dimensional datasets, while simultaneously offering greater computational efficiency.

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