Aspect-based sentiment analysis aims to predict sentiment polarity with fine granularity. While Graph Convolutional Networks (GCNs) are widely utilized for sentimental feature extraction, their naive application for syntactic feature extraction can compromise information preservation. This study introduces an innovative edge-enhanced GCN, named SentiSys, to navigate the syntactic graph while preserving intact feature information, leading to enhanced performance. Specifically,we first integrate a bidirectional long short-term memory (Bi-LSTM) network and a self-attention-based transformer. This combination facilitates effective text encoding, preventing the loss of information and predicting long dependency text. A bidirectional GCN (Bi-GCN) with message passing is then employed to encode relationships between entities. Additionally, unnecessary information is filtered out using an aspect-specific masking technique. To validate the effectiveness of our proposed model, we conduct extensive evaluation experiments and ablation studies on four benchmark datasets. The results consistently demonstrate improved performance in aspect-based sentiment analysis when employing SentiSys. This approach successfully addresses the challenges associated with syntactic feature extraction, highlighting its potential for advancing sentiment analysis methodologies.

翻译：方面级情感分析旨在以细粒度预测情感极性。尽管图卷积网络被广泛应用于情感特征提取，但其在句法特征提取中的朴素应用可能损害信息保留。本研究引入了一种创新的边增强图卷积网络——SentiSys，用于在保持完整特征信息的同时导航句法图，从而提升性能。具体而言，我们首先集成了双向长短期记忆网络和基于自注意力机制的Transformer。这种组合实现了有效的文本编码，防止信息丢失并预测长距离依赖文本。随后采用带有消息传递的双向图卷积网络对实体间关系进行编码。此外，通过方面特定掩码技术过滤不必要信息。为验证所提模型的有效性，我们在四个基准数据集上进行了广泛的评估实验和消融研究。结果表明，使用SentiSys时，方面级情感分析的性能持续提升。该方法成功解决了句法特征提取相关的挑战，凸显了其推进情感分析方法的潜力。