A text analysis system specializes in parsing LinkedIn humble brags and calculating authenticity scores. It detects patterns like I'm humbled to announce followed by obviously impressive achievements. The system now generates automated humble brags for people too busy to craft their own manufactured modesty. Your task: Parse professional humility while detecting genuine achievement vs manufactured modesty.
Why You're Doing This
You're building a sentiment analysis system that detects inauthentic humility patterns and calculates sincerity scores. This tests natural language processing, pattern recognition, sentiment analysis, and detecting manipulative language. It's like spam detection but for people who think announcing promotions requires performative modesty.
Take the W
✓ Identifies humble brag patterns accurately
✓ Calculates authenticity scores based on humility vs achievement ratio
✓ Distinguishes between genuine modesty and performative humility
Hard L
✗ Flags genuine humility as fake
✗ Misses obvious humble brags
✗ Produces authenticity scores without logical basis
Edge Cases
⚠ Genuinely modest person announcing major achievement without performative language
⚠ Obvious show-off using no humility language creating honest arrogance
⚠ Cultural differences in expressing professional success appropriately
⚠ Humble brag about being humble creating meta-manufactured-modesty
⚠ Achievement that requires humility language due to sensitive nature
Input Format:
LinkedIn post content with humility language analysis and achievement assessment
Expected Output:
Authenticity scoring with humble brag classification and sincerity evaluation
Example:
Post: Humbled to share that I just closed a $50M deal; Indicators: humbled; Achievement: extreme magnitude → Authenticity: 15/100; Classification: classic_humble_brag; Assessment: manufactured_modesty