首个跨语言情感维度立场分析数据集，支持多语种细粒度立场理解。
DimStance: Multilingual Datasets for Dimensional Stance Analysis
- 提出基于情绪维度的立场分析框架，用情绪值与唤醒度建模立场
- 构建包含1.17万条标注的多语言数据集，覆盖5种语言和2个领域
- 适用于需要情感感知的跨语言立场分析研究者
立场检测通常将作者态度划分为支持、中立或反对等类别。本文引入情感科学中的连续维度框架，以情绪值（正负）和唤醒度（平静-活跃）建模立场，捕捉更细微的情感状态。为此，我们提出首个具有情绪维度标注的数据集DimStance，包含11,746个目标方面，来自7,365篇跨语言文本（英语、德语、中文、尼日利亚皮钦语、斯瓦希里语），涵盖政治与环境保护两个领域。为评估情绪维度立场预测，我们定义了维度立场回归任务，分析跨语言情绪模式，并在回归与提示设置下对预训练模型进行基准测试。结果表明，微调的大语言模型在回归任务中表现良好，但低资源语言仍面临挑战，且基于标记生成的方法存在局限。DimStance为多语言、情感感知的立场分析提供了基础与评测平台。
原文摘要 · Abstract (English)
Stance detection is an established task that classifies an author's attitude toward a specific target into categories such as Favor, Neutral, and Against. Beyond categorical stance labels, we leverage a long-established affective science framework to model stance along real-valued dimensions of valence (negative-positive) and arousal (calm-active). This dimensional approach captures nuanced affective states underlying stance expressions, enabling fine-grained stance analysis. To this end, we introduce DimStance, the first dimensional stance resource with valence-arousal (VA) annotations. This resource comprises 11,746 target aspects in 7,365 texts across five languages (English, German, Chinese, Nigerian Pidgin, and Swahili) and two domains (politics and environmental protection). To facilitate the evaluation of stance VA prediction, we formulate the dimensional stance regression task, analyze cross-lingual VA patterns, and benchmark pretrained and large language models under regression and prompting settings. Results show competitive performance of fine-tuned LLM regressors, persistent challenges in low-resource languages, and limitations of token-based generation. DimStance provides a foundation for multilingual, emotion-aware, stance analysis and benchmarking.
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