多智能体框架提升社交媒体情感分析准确率
SentiMM: A Multimodal Multi-Agent Framework for Sentiment Analysis in Social Media
- 分角色处理文本与图像，协同融合多模态信息
- 在七类细粒度情感上超越现有模型表现
- 适合需要高精度多模态情感识别的场景
随着社交媒体上多模态内容日益增多，情感分析面临异构数据处理和多标签情绪识别的挑战。现有方法普遍缺乏有效的跨模态融合与外部知识整合。本文提出SentiMM，一种新型多智能体框架，通过专用智能体处理文本与视觉输入，融合多模态特征，借助知识检索丰富上下文，并聚合结果进行最终情感分类。同时构建了SentiMMD，一个大规模多模态数据集，包含七种细粒度情感类别。大量实验表明，SentiMM在性能上优于当前最优基线，验证了该结构化方法的有效性。
原文摘要 · Abstract (English)
With the increasing prevalence of multimodal content on social media, sentiment analysis faces significant challenges in effectively processing heterogeneous data and recognizing multi-label emotions. Existing methods often lack effective cross-modal fusion and external knowledge integration. We propose SentiMM, a novel multi-agent framework designed to systematically address these challenges. SentiMM processes text and visual inputs through specialized agents, fuses multimodal features, enriches context via knowledge retrieval, and aggregates results for final sentiment classification. We also introduce SentiMMD, a large-scale multimodal dataset with seven fine-grained sentiment categories. Extensive experiments demonstrate that SentiMM achieves superior performance compared to state-of-the-art baselines, validating the effectiveness of our structured approach.
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