Scientific reports multi relational dual attention graph transformer for fine grained sentiment analysis

Area of Science:

• Natural Language Processing
• Artificial Intelligence
• Computational Linguistics

Background:

• Aspect-Based Sentiment Analysis (ABSA) necessitates modeling complex linguistic dependencies.
• Existing graph-based methods struggle with multi-relational data and interpretability.
• Lack of effective attention mechanisms hinders precise sentiment polarity identification.

Purpose of the Study:

• To propose a novel framework, the Multi-Relational Dual-Attention Graph Transformer (MRDAGT), for enhanced ABSA.
• To effectively integrate syntactic, semantic, and discourse-level relations within a unified graph architecture.
• To improve the interpretability and performance of sentiment analysis models.

Main Methods:

• Developed a novel graph architecture unifying multiple relation types.
• Implemented a dual-attention mechanism for balanced local and aspect-focused interactions.
• Incorporated attention regularization (entropy-based penalties, L1 sparsity) for interpretability.

Main Results:

• MRDAGT establishes new state-of-the-art benchmarks across multiple ABSA datasets.
• Achieved substantial performance improvements in sentiment polarity identification.
• Demonstrated transparent and linguistically grounded prediction processes.

Conclusions:

• The proposed MRDAGT framework significantly advances ABSA.
• The dual-attention and regularization techniques enhance both performance and interpretability.
• MRDAGT offers a robust solution for real-world sentiment analysis applications.