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Related Experiment Video

Updated: Jan 15, 2026

Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation
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Protocol for Data Collection and Analysis Applied to Automated Facial Expression Analysis Technology and Temporal Analysis for Sensory Evaluation

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A unified dual-view knowledge-guided sentiment interaction networks for aspect-based sentiment analysis.

Xuejian Gao1, Fang'ai Liu1, Xuqiang Zhuang2

  • 1School of Information Science and Engineering, Shandong Normal University, Jinan, Shandong, 250014, China.

Neural Networks : the Official Journal of the International Neural Network Society
|October 12, 2025
PubMed
Summary

This study introduces the Dual-view Knowledge Guided Sentiment Interaction Network (Dual-KGIN) for improved aspect-based sentiment analysis. Dual-KGIN enhances sentiment semantics and syntax using external knowledge, achieving superior performance on benchmark datasets.

Keywords:
Aspect-based sentiment analysisExternal knowledgeGraph convolutional networksHierarchical knowledge interaction

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Area of Science:

  • Natural Language Processing
  • Artificial Intelligence
  • Machine Learning

Background:

  • Aspect-based sentiment analysis (ABSA) identifies sentiment towards specific entities and attributes.
  • Existing methods struggle to effectively integrate external knowledge for augmenting sequence semantics and capturing sentiment-syntax relationships.

Purpose of the Study:

  • To propose the Dual-view Knowledge Guided Sentiment Interaction Network (Dual-KGIN) to address limitations in ABSA.
  • To enhance both sentiment semantics and syntactic representations by integrating external knowledge and hierarchical interactions.

Main Methods:

  • Developed an external knowledge-guided syntactic Graph Convolutional Network (GCN) module to refine dependencies and enhance syntactic features.
  • Augmented sequence semantics using external knowledge with an attention mechanism.
  • Introduced a multi-level feature interaction module for improved sentiment representation.

Main Results:

  • Dual-KGIN demonstrated superior performance in aspect-specific sentiment identification on benchmark ABSA datasets.
  • Ablation studies confirmed the effectiveness of individual components within the Dual-KGIN framework.

Conclusions:

  • Dual-KGIN effectively integrates external knowledge and models feature interactions for state-of-the-art ABSA.
  • The proposed network offers a unified framework for enhancing sentiment semantics and syntactic representations.