SentiMM: A Multimodal Multi-Agent Framework for Sentiment Analysis in Social Media
This work addresses sentiment analysis for social media users by improving multimodal processing, though it appears incremental with a novel framework and dataset.
The paper tackled the problem of sentiment analysis in social media by addressing challenges in processing multimodal data and recognizing multi-label emotions, resulting in the SentiMM framework that achieves superior performance compared to state-of-the-art baselines.
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.