multimodal sentiment

**Multimodal sentiment analysis** combines information from **multiple communication channels** — text, audio/speech, and visual/facial cues — to determine a person's sentiment or emotional state more accurately than any single modality alone. **Why Multimodal Matters** - **Sarcasm Detection**: Text says "great job" (positive), but tone of voice is flat/mocking (negative). Audio resolves the ambiguity. - **Incongruent Signals**: A person says "I'm fine" (neutral text) while their face shows distress (negative visual). Visual cues reveal true sentiment. - **Rich Context**: Combining all channels provides a more complete understanding, similar to how humans naturally read emotions from multiple cues simultaneously. **Modalities and Features** - **Text**: Word choice, syntax, semantic meaning, sentiment keywords. - **Audio**: Pitch (fundamental frequency), energy, speaking rate, voice quality, pauses. Prosodic features carry emotional information beyond words. - **Visual**: Facial expressions (action units), eye contact, head movements, gestures, posture. **Fusion Approaches** - **Early Fusion**: Concatenate features from all modalities into a single vector before classification. Simple but may not capture inter-modal interactions. - **Late Fusion**: Process each modality independently with separate models, then combine their predictions. Each modality contributes its own "vote." - **Hybrid Fusion**: Extract modality-specific features, then use attention mechanisms or cross-modal transformers to learn interactions. - **Cross-Modal Attention**: Allow each modality to attend to relevant features in other modalities — text attending to audio pitch when processing potentially sarcastic words. **Datasets** - **CMU-MOSI**: 2,199 opinion segments from YouTube videos with text, audio, and visual annotations. - **CMU-MOSEI**: 23,454 segments — larger and more diverse than MOSI. - **IEMOCAP**: Multimodal emotional speech database with detailed annotations. **Applications** - **Customer Service**: Analyze video calls to detect customer frustration before it escalates. - **Mental Health**: Monitor patients through multiple channels for signs of depression or anxiety. - **Video Content Analysis**: Automatically assess the emotional tone of video content for recommendation systems. - **Human-Robot Interaction**: Robots that understand human emotions through speech, face, and body language. Multimodal sentiment analysis is **closer to human perception** than text-only analysis — humans naturally integrate verbal and non-verbal cues, and multimodal AI aims to do the same.