用轻量版BERT分析虾皮商品评论，准确率94.8%且速度快55%以上。
Sentiment Analysis Of Shopee Product Reviews Using Distilbert
- 采用DistilBERT模型进行情感分类，兼顾效率与精度。
- 在百万级英文评论上达94.8%准确率，优于SVM的90.2%。
- 适合需要快速处理海量电商评论的场景，如平台运营与产品优化。
数字电商快速发展，导致在线平台积累了海量用户评论。作为东南亚最大电商平台之一，虾皮每天接收数百万条商品评论，蕴含丰富的客户满意度与偏好信息。人工分析效率低下，亟需计算方法支持。本研究使用轻量级Transformer模型DistilBERT对虾皮英文评论进行情感分类。数据集包含约一百万条预处理后的英文评论，基于distilbert-base-uncased模型训练。评估指标包括准确率、精确率、召回率和F1分数，并与BERT和SVM等基准模型对比。结果显示，DistilBERT准确率达94.8%，略低于BERT的95.3%，但显著高于SVM的90.2%，且计算时间减少超过55%。结果表明，DistilBERT在准确性与效率间取得良好平衡，适用于大规模电商平台的情感分析任务。
原文摘要 · Abstract (English)
The rapid growth of digital commerce has led to the accumulation of a massive number of consumer reviews on online platforms. Shopee, as one of the largest e-commerce platforms in Southeast Asia, receives millions of product reviews every day containing valuable information regarding customer satisfaction and preferences. Manual analysis of these reviews is inefficient, thus requiring a computational approach such as sentiment analysis. This study examines the use of DistilBERT, a lightweight transformer-based deep learning model, for sentiment classification on Shopee product reviews. The dataset used consists of approximately one million English-language reviews that have been preprocessed and trained using the distilbert-base-uncased model. Evaluation was conducted using accuracy, precision, recall, and F1-score metrics, and compared against benchmark models such as BERT and SVM. The results show that DistilBERT achieved an accuracy of 94.8%, slightly below BERT (95.3%) but significantly higher than SVM (90.2%), with computation time reduced by more than 55%. These findings demonstrate that DistilBERT provides an optimal balance between accuracy and efficiency, making it suitable for large scale sentiment analysis on e-commerce platforms. Keywords: Sentiment Analysis, DistilBERT, Shopee Reviews, Natural Language Processing, Deep Learning, Transformer Models.
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