sentiment-analysislisted

# 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: ```python mention_velocity = mentions_last_hour / baseline_hourly_mentions # > 3.0 = trending, > 10.0 = viral ``` **Sentiment Polarity** — Positive vs negative tone: ```python polarity = (positive_co