Amplifying Aspect-Sentence Awareness: A Novel Approach for Aspect-Based Sentiment Analysis

TL;DR

This paper introduces A3SN, a novel attention-based method that amplifies aspect-sentence awareness in transformer models to improve aspect-based sentiment analysis, effectively capturing subtle relationships and outperforming existing models.

Contribution

We propose A3SN, a new attention mechanism that enhances aspect-sentence awareness in transformers for better ABSA performance, addressing limitations of previous syntactic integration methods.

Findings

A3SN outperforms state-of-the-art models on three benchmark datasets.

Amplified attention improves the capture of aspect-sentence relationships.

Gated fusion effectively combines multiple feature representations.

Abstract

Aspect-Based Sentiment Analysis (ABSA) is increasingly crucial in Natural Language Processing (NLP) for applications such as customer feedback analysis and product recommendation systems. ABSA goes beyond traditional sentiment analysis by extracting sentiments related to specific aspects mentioned in the text; existing attention-based models often need help to effectively connect aspects with context due to language complexity and multiple sentiment polarities in a single sentence. Recent research underscores the value of integrating syntactic information, such as dependency trees, to understand long-range syntactic relationships better and link aspects with context. Despite these advantages, challenges persist, including sensitivity to parsing errors and increased computational complexity when combining syntactic and semantic information. To address these issues, we propose Amplifying Aspect-Sentence Awareness (A3SN), a novel technique designed to enhance ABSA through amplifying aspect-sentence awareness attention. Following the transformer's standard process, our innovative approach incorporates multi-head attention mechanisms to augment the model with sentence and aspect semantic information. We added another multi-head attention module: amplify aspect-sentence awareness attention. By doubling its focus between the sentence and aspect, we effectively highlighted aspect importance within the sentence context. This enables accurate capture of subtle relationships and dependencies. Additionally, gated fusion integrates feature representations from multi-head and amplified aspect-sentence awareness attention mechanisms, which is essential for ABSA. Experimental results across three benchmark datasets demonstrate A3SN's effectiveness and outperform state-of-the-art (SOTA) baseline models.