SI-GCN: Modeling Specific-Aspect and Inter-Aspect Graph Convolutional Networks for Aspect-Level Sentiment Analysis

Aspect-level sentiment analysis (ALSA) aims to identify the sentiment polarity associated with specific aspects in textual data. However, existing methods utilizing graph convolutional networks (GCNs) face significant challenges, particularly in analyzing sentiments for multi-word aspects and captur...

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Bibliographic Details
Published in:Symmetry (Basel) 2024-12, Vol.16 (12), p.1687
Main Authors: Huang, Zexia, Zhu, Yihong, Hu, Jinsong, Chen, Xiaoliang
Format: Article
Language:English
Subjects:
Accuracy
Analysis
Artificial neural networks
aspect-level sentiment analysis
commonsense knowledge graph
Computational linguistics
Datasets
graph convolutional network
Knowledge management
Language processing
Natural language interfaces
Natural language processing
Neural networks
Restaurants
Semantics
Sentiment analysis
Social networks
syntax dependency tree
Words (language)
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Summary:Aspect-level sentiment analysis (ALSA) aims to identify the sentiment polarity associated with specific aspects in textual data. However, existing methods utilizing graph convolutional networks (GCNs) face significant challenges, particularly in analyzing sentiments for multi-word aspects and capturing sentiment relationships across multiple aspects in complex sentences. To address these issues, we introduce the Specific-aspect and Inter-aspect Graph Convolutional Network (SI-GCN), which integrates contextual information, syntactic dependencies, and commonsense knowledge to provide a robust solution. The SI-GCN model incorporates several innovative components: a Specific-aspect GCN module that effectively captures sentiment features for individual aspects; a knowledge-enhanced heterogeneous graph designed to manage implicit sentiment expressions and multi-word aspects; and a dual affine attention mechanism that accurately models inter-aspect relationships. Compared to existing state-of-the-art methods, the SI-GCN achieves improvements in performance ranging from 0.9% to 2.3% across four benchmark datasets. A detailed analysis of text semantics shows that the SI-GCN excels in challenging scenarios, including those involving aspects without explicit sentiment indicators, multi-word aspects, and informal language structures.
ISSN:2073-8994
2073-8994
DOI:10.3390/sym16121687