DanceHA: A Multi-Agent Framework for Document-Level Aspect-Based Sentiment Analysis

TL;DR

DanceHA introduces a multi-agent framework for document-level aspect-based sentiment analysis, effectively decomposing complex tasks and incorporating human-AI collaboration, with a new dataset highlighting informal writing styles.

Contribution

We propose DanceHA, a novel multi-agent framework for document-level ABSIA, and release Inf-ABSIA, a high-quality dataset emphasizing informal writing styles.

Findings

Effective decomposition of long-context ABSIA tasks.

Successful transfer of multi-agent knowledge to student models.

Highlighting the significance of informal styles in sentiment analysis.

Abstract

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.