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Area of Science:

  • Natural Language Processing
  • Neurorobotics

Background:

  • Aspect-level sentiment analysis (ABSA) is crucial for understanding nuanced emotions in text.
  • Limited training data and feature integration issues hinder current ABSA models.
  • Previous methods using graph convolutions face gradient explosion problems.

Purpose of the Study:

  • To investigate ChatGPT for aspect-level text augmentation.
  • To introduce an improved gated attention mechanism for graph convolutional networks.
  • To enhance the integration of semantic and syntactic features for ABSA.

Main Methods:

  • Leveraging ChatGPT for aspect-level text augmentation.
  • Developing an improved gated attention mechanism to mitigate gradient explosion in graph convolutional networks.
  • Enriching dependency graph features with a sentiment knowledge base and employing cross-fusion for feature integration.

Main Results:

  • The proposed model demonstrates superior performance compared to baseline models.
  • Effective integration of semantic and syntactic features was achieved.
  • The improved gated attention mechanism successfully addressed gradient explosion.

Conclusions:

  • The study presents a novel approach to ABSA using ChatGPT and an enhanced graph convolutional network.
  • The findings highlight the effectiveness of the proposed methods in overcoming data scarcity and feature integration challenges.
  • The model shows significant improvements in sentiment polarity prediction accuracy.