Emotion-Enhanced Multi-Task Learning with LLMs for Aspect Category Sentiment Analysis

TL;DR

This paper presents an emotion-enhanced multi-task learning framework for aspect category sentiment analysis that incorporates Ekman's basic emotions and VAD-based refinement, leading to improved performance.

Contribution

It introduces a novel approach that jointly models sentiment and emotions using LLMs, with a refinement mechanism for emotion accuracy, advancing ACSA methods.

Findings

Significant performance improvements over baselines on benchmark datasets.

Effective integration of Ekman's emotions and VAD refinement enhances sentiment analysis.

Demonstrates the importance of affective signals in fine-grained sentiment tasks.

Abstract

Aspect category sentiment analysis (ACSA) has achieved remarkable progress with large language models (LLMs), yet existing approaches primarily emphasize sentiment polarity while overlooking the underlying emotional dimensions that shape sentiment expressions. This limitation hinders the model's ability to capture fine-grained affective signals toward specific aspect categories. To address this limitation, we introduce a novel emotion-enhanced multi-task ACSA framework that jointly learns sentiment polarity and category-specific emotions grounded in Ekman's six basic emotions. Leveraging the generative capabilities of LLMs, our approach enables the model to produce emotional descriptions for each aspect category, thereby enriching sentiment representations with affective expressions. Furthermore, to ensure the accuracy and consistency of the generated emotions, we introduce an emotion refinement mechanism based on the Valence-Arousal-Dominance (VAD) dimensional framework. Specifically, emotions predicted by the LLM are projected onto a VAD space, and those inconsistent with their corresponding VAD coordinates are re-annotated using a structured LLM-based refinement strategy. Experimental results demonstrate that our approach significantly outperforms strong baselines on all benchmark datasets. This underlines the effectiveness of integrating affective dimensions into ACSA.