Aspect-Based Sentiment Analysis (ABSA) focuses on extracting sentiment at a fine-grained aspect level and has been widely applied across real-world domains. However, existing ABSA research relies on coarse-grained categorical labels (e.g., positive, negative), which limits its ability to capture nuanced affective states. To address this limitation, we adopt a dimensional approach that represents sentiment with continuous valence-arousal (VA) scores, enabling fine-grained analysis at both the aspect and sentiment levels. To this end, we introduce DimABSA, the first multilingual, dimensional ABSA resource annotated with both traditional ABSA elements (aspect terms, aspect categories, and opinion terms) and newly introduced VA scores. This resource contains 76,958 aspect instances across 42,590 sentences, spanning six languages and four domains. We further introduce three subtasks that combine VA scores with different ABSA elements, providing a bridge from traditional ABSA to dimensional ABSA. Given that these subtasks involve both categorical and continuous outputs, we propose a new unified metric, continuous F1 (cF1), which incorporates VA prediction error into standard F1. We provide a comprehensive benchmark using both prompted and fine-tuned large language models across all subtasks. Our results show that DimABSA is a challenging benchmark and provides a foundation for advancing multilingual dimensional ABSA.

翻译：方面级情感分析（ABSA）专注于在细粒度的方面层面提取情感，已在现实世界的多个领域得到广泛应用。然而，现有的ABSA研究依赖于粗粒度的分类标签（例如积极、消极），这限制了其捕捉细微情感状态的能力。为解决这一局限，我们采用了一种维度方法，使用连续的效价-唤醒度（VA）分数来表示情感，从而在方面和情感两个层面实现细粒度分析。为此，我们引入了DimABSA，这是首个多语言的维度ABSA资源，同时标注了传统的ABSA要素（方面术语、方面类别和观点术语）以及新引入的VA分数。该资源包含来自42,590个句子的76,958个方面实例，涵盖六种语言和四个领域。我们进一步提出了三个子任务，将VA分数与不同的ABSA要素相结合，为从传统ABSA过渡到维度ABSA提供了桥梁。鉴于这些子任务同时涉及分类和连续输出，我们提出了一种新的统一评估指标——连续F1（cF1），该指标将VA预测误差纳入标准F1计算中。我们使用提示式和微调的大型语言模型在所有子任务上提供了全面的基准测试。结果表明，DimABSA是一个具有挑战性的基准，并为推进多语言维度ABSA研究奠定了基础。