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.