sentiment-analysis

name	sentiment-analysis
description	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:

mention_velocity = mentions_last_hour / baseline_hourly_mentions
# > 3.0 = trending, > 10.0 = viral

Sentiment Polarity — Positive vs negative tone:

polarity = (positive_count - negative_count) / total_count
# Range: -1.0 (all negative) to +1.0 (all positive)

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:

social_volume_z = (current_volume - mean_30d) / std_30d
# z > 2.0 suggests unusual activity

On-Chain Sentiment Proxies

On-chain data reveals what participants are doing, not just saying:

Funding Rates — Perpetual futures cost of carry:

# Positive funding = longs pay shorts (bullish crowding)
# Negative funding = shorts pay longs (bearish crowding)
funding_sentiment = -1.0 * normalize(funding_rate, -0.1, 0.1)
# Inverted: high positive funding is contrarian bearish

Long/Short Ratio — Proportion of leveraged positions:

ls_ratio = long_accounts / short_accounts
# > 2.0 = crowded long, < 0.5 = crowded short
ls_sentiment = -1.0 * normalize(ls_ratio, 0.5, 2.0)

Exchange Flows — Net deposits/withdrawals:

net_flow = exchange_inflows - exchange_outflows
# Positive net flow (deposits) = bearish (selling pressure)
# Negative net flow (withdrawals) = bullish (accumulation)
flow_sentiment = -1.0 * normalize(net_flow, -threshold, threshold)

Keyword-Based Sentiment Scoring

A simple, LLM-free approach using curated word lists:

BULLISH_KEYWORDS = {
    "moon": 2, "bullish": 2, "pump": 1, "breakout": 2,
    "buy": 1, "long": 1, "accumulate": 2, "undervalued": 2,
    "gem": 1, "rocket": 1, "ath": 1, "rally": 2,
}
BEARISH_KEYWORDS = {
    "dump": 2, "bearish": 2, "crash": 2, "scam": 3,
    "rug": 3, "sell": 1, "short": 1, "overvalued": 2,
    "dead": 2, "rekt": 1, "ponzi": 3, "exit": 1,
}

def score_text(text: str) -> float:
    """Score text from -1.0 (bearish) to +1.0 (bullish)."""
    words = text.lower().split()
    bull_score = sum(BULLISH_KEYWORDS.get(w, )  w  words)
    bear_score = (BEARISH_KEYWORDS.get(w, )  w  words)
    total = bull_score + bear_score
     total == :
         
     (bull_score - bear_score) / total

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:

def composite_sentiment(
    social_polarity: float,    # -1.0 to +1.0
    mention_velocity: float,   # 0 to inf
    fear_greed: int,           # 0 to 100
    funding_rate: float,       # -0.1 to +0.1
    weights: dict | None = None,
) -> float:
    """Compute weighted composite sentiment score (-100 to +100).

    Args:
        social_polarity: Average polarity of social mentions.
        mention_velocity: Current velocity vs baseline.
        fear_greed: Fear & Greed index reading.
        funding_rate: Current perpetual funding rate.
        weights: Optional custom weights.

    Returns:
        Composite score from -100 (extreme fear) to +100 (extreme greed).
    """
    w = weights or {
        "social": 0.30,
        "velocity": 0.15,
        "fear_greed": 0.30,
        "funding": 0.25,
    }
    # Normalize each component to -1.0 to +1.0
    s_social = social_polarity
    s_velocity = min(mention_velocity / 10.0, 1.0)  # Cap at 10x
    s_fg = (fear_greed - 50) / 50.0  # 0-100 -> -1 to +1
    s_funding = -10.0 * funding_rate  # Contrarian: high funding = bearish
    s_funding = max(-1.0, min(1.0, s_funding))

    raw = (
        w["social"] * s_social
        + w["velocity"] * s_velocity
        + w[] * s_fg
        + w[] * s_funding
    )
     (raw * , )

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:

def influencer_signal(
    posts: list[dict],
    min_followers: int = 50_000,
    lookback_hours: int = 24,
) -> dict:
    """Detect influencer activity around a token.

    Args:
        posts: List of posts with 'followers', 'timestamp', 'sentiment'.
        min_followers: Minimum follower count to qualify as influencer.
        lookback_hours: Time window in hours.

    Returns:
        Dict with influencer_count, avg_sentiment, total_reach.
    """
    cutoff = time.time() - (lookback_hours * 3600)
    relevant = [
        p for p in posts
        if p["followers"] >= min_followers and p["timestamp"] >= cutoff
    ]
    if not relevant:
        return {"influencer_count": 0, "avg_sentiment": 0.0, "total_reach": 0}
    return {
        "influencer_count": len(relevant),
        "avg_sentiment": sum(p["sentiment"] for p in relevant) / len(relevant),
        "total_reach": sum(p["followers"] for p in relevant),
    }

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

1. Fetch fear/greed index          → Market-wide mood
2. Pull social data for token      → Token-specific sentiment
3. Score text with keyword method  → Polarity scores
4. Compute mention velocity        → Trending detection
5. Check on-chain proxies          → Funding, flows
6. Calculate composite score       → Single decision input
7. Flag contrarian signals         → Extreme readings
8. Integrate with position sizing  → Adjust allocation

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