| 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
Sentiment Polarity — Positive vs negative tone:
polarity = (positive_count - negative_count) / total_count
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
On-Chain Sentiment Proxies
On-chain data reveals what participants are doing, not just saying:
Funding Rates — Perpetual futures cost of carry:
funding_sentiment = -1.0 * normalize(funding_rate, -0.1, 0.1)
Long/Short Ratio — Proportion of leveraged positions:
ls_ratio = long_accounts / short_accounts
ls_sentiment = -1.0 * normalize(ls_ratio, 0.5, 2.0)
Exchange Flows — Net deposits/withdrawals:
net_flow = exchange_inflows - exchange_outflows
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,
mention_velocity: float,
fear_greed: int,
funding_rate: float,
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,
}
s_social = social_polarity
s_velocity = min(mention_velocity / 10.0, 1.0)
s_fg = (fear_greed - 50) / 50.0
s_funding = -10.0 * funding_rate
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 sourcesreferences/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 signalsscripts/keyword_sentiment.py — Standalone keyword-based text sentiment analyzer with synthetic demo data
