LSA: Modeling Aspect Sentiment Coherency via Local Sentiment Aggregation

TL;DR

This paper introduces a novel local sentiment aggregation (LSA) method that models aspect sentiment coherency to improve sentiment classification accuracy and enhance adversarial defense in aspect-based sentiment analysis.

Contribution

The paper proposes a new LSA paradigm based on differential-weighted sentiment aggregation, demonstrating state-of-the-art results and robustness in adversarial scenarios.

Findings

Outperforms existing models on five datasets

Achieves state-of-the-art sentiment classification accuracy

Enhances adversarial defense capabilities

Abstract

Aspect sentiment coherency is an intriguing yet underexplored topic in the field of aspect-based sentiment classification. This concept reflects the common pattern where adjacent aspects often share similar sentiments. Despite its prevalence, current studies have not fully recognized the potential of modeling aspect sentiment coherency, including its implications in adversarial defense. To model aspect sentiment coherency, we propose a novel local sentiment aggregation (LSA) paradigm based on constructing a differential-weighted sentiment aggregation window. We have rigorously evaluated our model through experiments, and the results affirm the proficiency of LSA in terms of aspect coherency prediction and aspect sentiment classification. For instance, it outperforms existing models and achieves state-of-the-art sentiment classification performance across five public datasets. Furthermore, we demonstrate the promising ability of LSA in ABSC adversarial defense, thanks to its sentiment coherency modeling. To encourage further exploration and application of this concept, we have made our code publicly accessible. This will provide researchers with a valuable tool to delve into sentiment coherency modeling in future research.