Glossary
Sentiment Analysis
Understanding emotional tone in text/media. Sentient extracts true sentiment through context, not just keywords.
Definition
Sentiment Analysis measures the emotional tone-positive, negative, neutral-in text and media. Conventional sentiment analysis relies on keyword lexicons and simple scoring. Sentient OS extracts true sentiment through contextual and semantic analysis: NLP models understand that 'This is great' can be sincere or sarcastic depending on context. Tonality analysis extends sentiment to emotional nuance. Sentiment informs the Psychographic Layer (audience emotional state), content intelligence (content resonance), and brand safety (historical sentiment tracking). Sentiment is one input to computational empathy-understanding the emotional undertone behind engagement.
Why It Matters
Sentiment analysis informs computational empathy and content intelligence. Sentient understands emotional tone in context-not just positive/negative keywords.
Related Pages
Related Terms
Tonality Analysis
Evaluating emotional undertone through context and semantic analysis. Distinguishing sarcasm from enthusiasm.
Computational Empathy
Technology that models human beliefs and resistances. Understanding the 'Why' behind behavior.
Natural Language Processing (NLP)
AI technology for understanding human language in context. Used in Layer 2 for intent and tonality analysis.
Content Intelligence
Understanding content resonance, format effectiveness, and semantic alignment with audiences.
Brand Safety
Ensuring ads/content appear in appropriate environments. Historical brand sentiment tracking for safety assessment.
Explore the Full Platform
See how these concepts come to life inside Sentient OS.