Modeling Inter-Aspect Dependencies with a Non-temporal Mechanism for Aspect-Based Sentiment Analysis

TL;DR

This paper introduces a non-temporal mechanism to model inter-aspect dependencies in aspect-based sentiment analysis, addressing the limitations of temporal dependency assumptions and class imbalance, resulting in improved performance.

Contribution

It proposes a novel non-temporal approach for inter-aspect dependency modeling and tackles class imbalance in ABSA, showing effectiveness across multiple domains.

Findings

Improved accuracy on SemEval 2014 datasets

Effective handling of class imbalance

Demonstrated across multiple domains

Abstract

For multiple aspects scenario of aspect-based sentiment analysis (ABSA), existing approaches typically ignore inter-aspect relations or rely on temporal dependencies to process aspect-aware representations of all aspects in a sentence. Although multiple aspects of a sentence appear in a non-adjacent sequential order, they are not in a strict temporal relationship as natural language sequence, thus the aspect-aware sentence representations should not be treated as temporal dependency processing. In this paper, we propose a novel non-temporal mechanism to enhance the ABSA task through modeling inter-aspect dependencies. Furthermore, we focus on the well-known class imbalance issue on the ABSA task and address it by down-weighting the loss assigned to well-classified instances. Experiments on two distinct domains of SemEval 2014 task 4 demonstrate the effectiveness of our proposed approach.