MASS: Mitigating Aspect-Oriented Semantic Sparsity for Fine-Grained Sentiment Analysis

Aspect-based sentiment analysis (ABSA) is a fine-grained sentiment classification task. Despite significant improvements in this field, progress is hindered by challenges such as the sparsity of contexts for specific aspects, interference from irrelevant words within sentences, and a lack of research on leveraging correlations between samples. In order to address these issues, we propose a novel framework MASS, Mitigating Aspect-oriented Semantic Sparsity for ABSA. Firstly, we employ advanced prompting techniques with Large Language Models (LLMs) to generate nuanced aspect-specific descriptions, thereby enhancing contexts related to the aspect. Subsequently, we design a novel fusion module aimed at seamlessly integrating aspectual insights with the original sentence. Finally, we develop three pioneering contrastive learning strategies to explore and learn complex correlations between samples, which are crucial for fine-grained sentiment analysis. Experiments on six benchmark datasets demonstrate that our MASS substantially outperforms state-of-the-art techniques and provides valuable insights for applying LLMs to downstream tasks.