Aspect-Dimension Sentiment Analysis for Short Chinese E-Commerce Reviews: A Comparative Study of Lightweight CNN, LSTM and Transformer Models
TL;DR
This study adopts aspect-based sentiment analysis (ABSA) as the core framework and constructs a labeling schema comprising four sentiment dimensions and three polarity levels, yielding 12 classification categories, providing quantitative evidence for model selection under diverse deployment constraints.
Abstract
The rapid growth of online shopping platforms has significantly increased the analytical value of product review data. Short reviews on Chinese e-commerce platforms, typically containing fewer than 50 characters, simultaneously address multiple dimensions, including product quality, logistics, customer service, and value for money, rendering conventional sentence-level sentiment classification insufficient for fine-grained analysis. This study adopts aspect-based sentiment analysis (ABSA) as the core framework and constructs a labeling schema comprising four sentiment dimensions and three polarity levels, yielding 12 classification categories. Rule-based automated annotation was applied to 62,770 publicly available Chinese consumer reviews; manual verification on a 500-sample subset confirmed an annotation accuracy of 87.2%. Three lightweight models—TextCNN, BiLSTM with attention (BiLSTM+Attn), and ERNIE 3.0-Nano—were trained and evaluated under identical experimental conditions on an NVIDIA RTX 4060 laptop GPU. ERNIE 3.0-Nano proved to be the most accurate, with an accuracy of 0.6363 and a macro-average F1 score of 0.5798. BiLSTM+Attn was the fastest on GPU: 0.004 ms per sample. TextCNN had the smallest parameter count: 0.69 million. These findings provide quantitative evidence for model selection under diverse deployment constraints.