提出多智能体框架DanceHA，解决长文档情感强度分析难题
DanceHA: A Multi-Agent Framework for Document-Level Aspect-Based Sentiment Analysis
- 用分而治之策略拆解长文本任务，多智能体协作完成分析
- 在新数据集Inf-ABSIA上实现高精度的细粒度情感强度提取
- 特别关注非正式写作风格对情感强度的影响，适合实际场景应用
基于方面的情感强度分析（ABSIA）受到越来越多关注，但研究主要集中在特定领域、句子级设置。相比之下，文档级ABSIA——尤其是提取方面-类别-观点-情感-强度（ACOSI）元组这类复杂任务——仍缺乏探索。本文提出DanceHA，一个面向开放式、文档级ABSIA的多智能体框架，适用于非正式写作风格。DanceHA包含两个核心组件：Dance采用分而治之策略，将长上下文的ABSIA任务分解为多个可管理的子任务，由专业化智能体协同完成；HA则实现人机协作标注。我们发布了Inf-ABSIA，一个跨领域的文档级ABSIA数据集，包含来自DanceHA的细粒度且高准确率标签。大量实验表明，该智能体框架有效，且DanceHA中的多智能体知识可成功迁移到学生模型。结果凸显了被忽视的非正式写作风格在ABSIA中的重要性，因其常强化特定方面的观点强度。
原文摘要 · Abstract (English)
Aspect-Based Sentiment Intensity Analysis (ABSIA) has garnered increasing attention, though research largely focuses on domain-specific, sentence-level settings. In contrast, document-level ABSIA--particularly in addressing complex tasks like extracting Aspect-Category-Opinion-Sentiment-Intensity (ACOSI) tuples--remains underexplored. In this work, we introduce DanceHA, a multi-agent framework designed for open-ended, document-level ABSIA with informal writing styles. DanceHA has two main components: Dance, which employs a divide-and-conquer strategy to decompose the long-context ABSIA task into smaller, manageable sub-tasks for collaboration among specialized agents; and HA, Human-AI collaboration for annotation. We release Inf-ABSIA, a multi-domain document-level ABSIA dataset featuring fine-grained and high-accuracy labels from DanceHA. Extensive experiments demonstrate the effectiveness of our agentic framework and show that the multi-agent knowledge in DanceHA can be effectively transferred into student models. Our results highlight the importance of the overlooked informal styles in ABSIA, as they often intensify opinions tied to specific aspects.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。