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.

翻译：方面类别情感分析（ACSA）借助大语言模型（LLMs）已取得显著进展，但现有方法主要聚焦于情感极性，而忽视了构成情感表达的内在情绪维度。这一局限阻碍了模型捕捉针对特定方面类别的细粒度情感信号的能力。为应对此局限，我们提出一种新颖的情感增强多任务ACSA框架，该框架基于Ekman的六种基本情绪，联合学习情感极性和类别特异性情绪。利用LLMs的生成能力，我们的方法使模型能够为每个方面类别生成情绪描述，从而通过情感表达丰富情感表征。此外，为确保生成情绪的准确性和一致性，我们引入了一种基于效价-唤醒度-支配度（VAD）维度框架的情绪精炼机制。具体而言，LLM预测的情绪被投影到VAD空间中，与对应VAD坐标不一致的情绪将通过一种基于结构化LLM的精炼策略进行重新标注。实验结果表明，我们的方法在所有基准数据集上均显著优于强基线模型，这凸显了将情感维度整合到ACSA中的有效性。