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Aspect-based sentiment analysis based on multi-granularity graph convolutional network.

Yanfen Cheng1, Minghui Yuan1, Fan He1

  • 1School of Computer Science and Artificial Intelligence, Wuhan University of Technology, Wuhan, 430070, China.

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|July 28, 2025
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Summary
This summary is machine-generated.

This study introduces a novel multi-granularity graph convolutional network (MGCN) for aspect sentiment classification. MGCN effectively utilizes local and global sentiment features from constituency trees and fuses dependency and constituency syntactic information for improved aspect-based sentiment analysis.

Keywords:
Aspect-based sentiment analysisConstituency treeDependency treeGraph convolutional networkNatural language processing

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Computational Linguistics

Background:

  • Aspect-based sentiment analysis (ABSA) aims to identify sentiment towards specific aspects within text.
  • Existing methods use attention mechanisms and graph convolutional networks (GCNs) but overlook local sentiment features and comprehensive syntactic information.
  • Current approaches often focus on global sentiment features within constituency trees, neglecting local nuances.

Purpose of the Study:

  • To propose a novel multi-granularity graph convolutional network (MGCN) for aspect sentiment classification (ASC).
  • To address limitations in existing ABSA methods by incorporating local sentiment features and fusing diverse syntactic information.
  • To enhance the predictive performance of aspect-based sentiment analysis by leveraging multi-granularity features.

Main Methods:

  • Developed a multi-granularity graph convolutional network (MGCN) with attention, mask matrix, and GCN layers.
  • Constructed semantic association matrices using an attention mechanism to capture word-sentence semantic relationships.
  • Designed hierarchical, rule-based multi-granularity constituency tree mask matrices (CM) for local-to-global feature extraction.
  • Fused dependency and constituency tree structures into multi-granularity fusion mask matrices (FM), enhanced by semantic associations.

Main Results:

  • The proposed MGCN demonstrated effectiveness in aspect sentiment classification tasks.
  • Experiments on SemEval 2014 and Twitter datasets validated the model's performance.
  • The integration of local sentiment features and fused syntactic information improved predictive accuracy.

Conclusions:

  • The novel MGCN effectively extracts sentiment features at multiple granularities from constituency trees.
  • Comprehensive utilization of syntactic information from both dependency and constituency trees enhances model performance.
  • MGCN offers a promising advancement in aspect-based sentiment analysis, particularly for aspect sentiment classification.