A Prompt-Driven framework for compensation and fusion in multimodal sentiment analysis with missing modalities

Key Points

• Multimodal sentiment analysis enhances performance, especially with missing modalities, indicating better outcomes.
• Key improvement includes 15% higher accuracy when compensating for missing data in mood detection tasks.
• Framework employs a prompt-driven approach to integrate various data sources effectively, including text and audio.
• Findings suggest that using this framework may enable more robust sentiment analysis, retaining critical insights despite gaps.