# Syntactic denoising and multi-strategy auxiliary enhancement for aspect-based sentiment analysis

## TL;DR

This paper introduces a new method for aspect-based sentiment analysis that improves performance by reducing syntactic noise and better combining syntactic and semantic information.

## Contribution

The novel SDMAE approach combines syntactic denoising and multi-strategy auxiliary learning to enhance aspect-based sentiment analysis.

## Key findings

- The proposed SDMAE method outperforms state-of-the-art models on four benchmark datasets.
- Syntactic denoising improves model robustness to irregular sentence structures.
- Multi-channel adaptive aggregation enhances the integration of syntactic and semantic features.

## Abstract

Aspect-based sentiment analysis (ABSA) aims to identify the sentiment polarity associated with specific aspect terms within sentences. Existing studies have primarily focused on constructing graphs from dependency trees of sentences to extract syntactic features. However, given that public datasets are often derived from online reviews, the syntactic structures of these sentences frequently exhibit irregularities. As a result, the performance of syntactic-based Graph Convolution Network (GCN) models is adversely impacted by the noise introduced during dependency parsing. Moreover, the interaction between syntactic and semantic information in these approaches is often insufficient, which significantly impairs the model’s ability to accurately detect sentiment.To address these challenges, we propose a novel approach called Syntactic Denoising with Multi-strategy Auxiliary Enhancement (SDMAE) for the ABSA task. Specifically, we prune the original dependency tree by focusing on context words with specific part-of-speech features that are critical for conveying the sentiment of aspect terms, and then construct the graph. We introduce a Multi-channel Adaptive Aggregation Module (MAAM), a feature aggregation system that employs a multi-head attention mechanism to integrate semantic and syntactic GCN output representations. Furthermore, we design a multi-strategy task learning framework that incorporates sentiment lexicons and supervised contrastive learning to enhance the model’s performance in aspect sentiment recognition.Comprehensive experiments conducted on four benchmark datasets demonstrate that our approach achieves significant performance improvements compared to several state-of-the-art methods across all evaluated datasets.