DanceHA: A Multi-Agent Framework for Document-Level Aspect-Based Sentiment Analysis
This work addresses document-level ABSIA, a complex task for sentiment analysis in informal texts, though it appears incremental as it builds on existing ABSIA concepts with a new framework.
The paper tackles the underexplored problem of document-level Aspect-Based Sentiment Intensity Analysis (ABSIA) for informal writing styles, introducing DanceHA, a multi-agent framework that decomposes the task and enables human-AI collaboration, resulting in the release of the Inf-ABSIA dataset and demonstrating effective knowledge transfer to student models.
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.