Abstract
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.
Abstract (translated)
随着社交媒体上多模态内容的日益普及，情感分析面临着在处理异质数据和识别多标签情绪方面的重要挑战。现有的方法往往缺乏有效的跨模式融合和外部知识整合能力。为此，我们提出了SentiMM，这是一个旨在系统地解决这些问题的新颖多代理框架。SentiMM通过专门的代理来处理文本和视觉输入，融合多模态特征，并通过知识检索丰富上下文信息，最后聚合结果以进行最终的情感分类。 为了支持这一研究，我们还引入了SentiMMD，这是一个包含七个细粒度情感类别的大规模多模态数据集。广泛的实验表明，与最先进的基线方法相比，SentiMM在性能上表现出显著优势，这验证了我们的结构化方法的有效性。
URL
https://arxiv.org/abs/2508.18108
