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The paper proposes SentiLLM, a framework that uses semantic-aligned structural abstraction to distill non-verbal modalities into text-like tokens for multimodal sentiment analysis with LLMs. It introduces a dual-stream salience-context calibration mechanism and achieves superior performance on four datasets.