SemMSA: Latent Semantic-Aided Robust Multimodal Sentiment Analysis with Incomplete Data
Recent research on Multimodal Sentiment Analysis (MSA) has focused on learning from language, visual, and acoustic modalities with incomplete data to infer human sentiment. Most studies typically compensate for missing information by reconstructing modality features or designing complicated fusion mechanisms. However, these methods still suffer from spurious generation and nois
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Recent research on Multimodal Sentiment Analysis (MSA) has focused on learning from language, visual, and acoustic modalities with incomplete data to infer human sentiment. Most studies typically compensate for missing information by reconstructing modality features or designing complicated fusion mechanisms. However, these methods still suffer from spurious generation and nois
This paper introduces a robust approach to Multimodal Sentiment Analysis (MSA) by leveraging LLMs for semantic grounding, which is crucial for handling incomplete data. ML engineers can apply these techniques to build more resilient and accurate multimodal systems, especially in scenarios with real-world data imperfections.