Market sentiment extraction from social media, news, and on-chain data including mention velocity, fear and greed indices, and influencer tracking

Sentiment Analysis Extract and quantify market sentiment from social media, news feeds, and on chain data to identify crowd positioning and potential contrarian opportunities. When to Use This Skill Gauge crowd sentiment before entering or exiting a position Detect euphoria/panic extremes that precede reversals Monitor social mention velocity for early trend detection Track influencer activity around specific tokens Build composite sentiment scores for systematic strategies Core Concepts Sentiment Data Sources Source Data Type Access Twitter/X Post text, engagement, follower counts API (paid tiers) Reddit Subreddit posts, comments, upvotes Reddit API Telegram Channel messages, member counts Bot API or scraping Discord Server activity, message volume Bot integration News Headlines, article text NewsAPI, RSS feeds CoinGecko Community stats, developer activity Free API Alternative.me Fear & Greed Index Free API On chain Funding rates, exchange flows Exchange APIs See references/data sources.md for complete API details, rate limits, and access patterns for each source. Sentiment Metrics Mention Velocity — Rate of token mentions over time: Sentiment Polarity — Positive vs negative tone: Fear & Greed Index — Composite market mood (0 100): Range Label Typical Signal 0 24 Extreme Fear Potential accumulation zone 25 44 Fear Below average sentiment 45 55 Neutral No strong directional bias 56 74 Greed Above average sentiment 75 100 Extreme Greed Potential distribution zone Social Volume — Total mentions across platforms: On Chain Sentiment Proxies On chain data reveals what participants are doing, not just saying: Funding Rates — Perpetual futures cost of carry: Long/Short Ratio — Proportion of leveraged positions: Exchange Flows — Net deposits/withdrawals: Keyword Based Sentiment Scoring A simple, LLM free approach using curated word lists: See references/scoring methods.md for the full methodology, temporal decay weighting, and composite score construction. Composite Sentiment Score Combine multiple signals into a single score: Contrarian Signals Extreme sentiment readings often precede reversals: Condition Interpretation Composite < 70 Extreme fear — historically a buying zone Composite +70 Extreme greed — historically a selling zone Velocity 10x + polarity 0.6 Euphoric spike — fade potential Velocity 10x + polarity < 0.6 Panic spike — bounce potential Funding 0.05% + LS ratio 2.0 Crowded long — liquidation risk Funding < 0.05% + LS ratio < 0.5 Crowded short — squeeze risk Key principle : Sentiment is most useful at extremes. Neutral readings (composite between 30 and +30) have low predictive value. Influencer Tracking Monitor high follower accounts for early signal detection: Integration With Other Skills Skill Integration Point position sizing Reduce size in extreme greed, increase in extreme fear risk management Tighten stops when sentiment diverges from price regime detection Sentiment confirms or contradicts regime classification feature engineering Sentiment metrics as ML features signal classification Sentiment as input to signal scoring models whale tracking Combine whale activity with social sentiment token holder analysis Holder growth/decline as sentiment proxy Practical Workflow Limitations and Warnings Sentiment is noisy. Individual readings are unreliable — use trends and extremes. Social data is gameable. Bot activity can inflate mention counts. Keyword scoring is crude. It misses sarcasm, context, and nuance. Lag exists. By the time sentiment is measurable, price may have moved. Not financial advice. Sentiment data is for informational and analytical purposes only. API access varies. Twitter/X API pricing has changed frequently. Budget accordingly. Survivorship bias. Tokens that go to zero stop being discussed — absence of mentions is also a signal. Files References references/data sources.md — API details, rate limits, and access patterns for all sentiment data sources references/scoring methods.md — Keyword lists, composite scoring methodology, temporal decay, contrarian logic Scripts scripts/sentiment scanner.py — Fetches live sentiment data from free APIs, computes composite scores, flags contrarian signals scripts/keyword sentiment.py — Standalone keyword based text sentiment analyzer with synthetic demo data

sentiment-analysis