QUANTIFYING THE VIBES: Bitcoin Sentiment Analysis, Memes, and Fiction | Michael Sullivan

— Sentiment analysis methodology: Sullivan has developed individualized sentiment analysis tools tracking Bitcoin figures and cohorts (OGs vs. newcomers, high-signal accounts vs. contrarians) to quantify mood shifts correlated with price movements and market cycles. He emphasizes analyzing individuals rather than aggregated noise to capture authentic signal.

— Blood of the Bourgeoisie: Sullivan's Bitcoin thriller uses fiction as an "orange peel" mechanism to introduce Bitcoin concepts to non-Bitcoin audiences. Written in 69 quick chapters with layered themes—the book works as a standalone thriller while embedding deeper Bitcoin philosophy and explores tensions between revolutionary frustration and pragmatic systemic change.

— Hedge fund methods applied to Bitcoin: Sullivan notes he accidentally recreated proprietary sentiment-tracking methods used quietly by hedge funds. These tools analyze language patterns, price-level discussions, and narrative spread to inform trading and macro decisions—techniques now being adapted for Bitcoin by various parties.

— Narrative propagation and memetic analysis: Tracking how Bitcoin ideas spread across the ecosystem—examining which accounts introduce new framing (e.g., Saylor on "digital credit") and watching that language proliferate through Twitter data and podcast appearances over time.

— Bitcoin exchange pivot to gambling: Analysis of exchange CEO language shows deliberate distancing from Bitcoin-native strategies (notably never mentioning MicroStrategy or Saylor), instead pivoting to prediction markets and stock trading—suggesting recognition that Bitcoin-only strategies outperform their legacy crypto casino models.

— AI as creative and analytical tool: Sullivan leverages AI models for sentiment classification, data architecture, and rapid iteration on visualization, enabling solo development of analysis that previously required teams and years. He emphasizes AI's role in crystallizing ideas through writing and language work.

— Sentiment as contrarian signal: Track established Bitcoin figures' mood shifts rather than aggregate sentiment. Historically, when long-term holders become significantly more optimistic than newer market participants, it has preceded opportune buying periods. Use sentiment divergence between cohorts as a vibe check, not a trading rule.

— Exposure is iterative, not instantaneous: Rather than expecting single "orange pill" moments to convert skeptics, recognize that Bitcoin adoption typically requires multiple, varied touchpoints over time. Different narrative frames (fiction, on-chain analysis, podcasts) serve different people at different stages; all contribute to eventual awareness.