A novel span and syntax enhanced large language model based framework for fine-grained sentiment analysis.

Area of Science:

• Natural Language Processing
• Artificial Intelligence
• Computational Linguistics

Background:

• Transformer-based large language models excel in NLP but struggle with explicit syntactic relationships and local term nuances.
• Fine-grained aspect-based sentiment analysis requires precise identification of aspect entities and their associated sentiment.
• Existing models face limitations in capturing the intricate linguistic details necessary for this task.

Purpose of the Study:

• To propose a novel framework that enhances large language models for fine-grained aspect-based sentiment analysis.
• To address the limitations of current models in modeling syntactic relationships and local nuances.
• To improve the accuracy of aspect entity recognition and sentiment classification.

Main Methods:

• Developed a joint learning framework integrating span-aware attention, contextual Transformer, and syntax-aware Transformer.
• These components generate span-aware, contextual, and syntax-aware features in parallel.
• A feature aggregation module dynamically fuses these features for a comprehensive representation.

Main Results:

• The proposed framework demonstrates superior performance on benchmark datasets for fine-grained aspect-based sentiment analysis.
• Experimental results show significant improvements compared to state-of-the-art baseline models.
• The architecture effectively leverages span, contextual, and syntax-aware features.

Conclusions:

• The novel framework represents a pioneering effort in augmenting large language models for aspect-based sentiment analysis.
• The integration of span and syntax awareness significantly enhances model capabilities.
• The approach offers a promising direction for future research in nuanced sentiment analysis.