sentiment-analysis
Market sentiment extraction from social media, news, and on-chain data including mention velocity, fear and greed indices, and influencer tracking
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Install
Works with every agent the skills CLI supports.
npx skills add https://github.com/agiprolabs/claude-trading-skills --skill sentiment-analysis- 01
Install
Run the command in your project. The CLI asks which agents to install for.
- 02
Describe the task
Ask your agent for something this skill covers. It loads sentiment-analysis automatically when relevant.
- 03
Review the result
The agent follows the SKILL.md steps, scripts and references bundled with the skill.
SKILL.md
The instructions your agent reads when the skill is triggered.
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) |
| 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, 0) for w in words)
bear_score = sum(BEARISH_KEYWORDS.get(w, 0) for w in words)
total = bull_score + bear_score
if total == 0:
return 0.0
return (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["fear_greed"] * s_fg
+ w["funding"] * s_funding
)
return round(raw * 100, 1)
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
FAQ
What does the sentiment-analysis skill do?
Market sentiment extraction from social media, news, and on-chain data including mention velocity, fear and greed indices, and influencer tracking
How do I install sentiment-analysis?
Run npx skills add https://github.com/agiprolabs/claude-trading-skills --skill sentiment-analysis in your project. The skills CLI detects the agents you use and copies the skill into the right folder. For Claude Code you can also place the skill folder at ~/.claude/skills/sentiment-analysis/ (personal) or .claude/skills/sentiment-analysis/ (project).
Which AI agents can use sentiment-analysis?
Any agent that supports the open Agent Skills format (a folder with a SKILL.md file), including Claude Code, Codex, Cursor, GitHub Copilot, Gemini CLI, Windsurf, Cline, OpenCode and more.
Is sentiment-analysis safe to install?
No partner security audit is available yet. Skills can run commands and scripts, so read the SKILL.md and any bundled files before installing.
Who maintains sentiment-analysis?
It is published by agiprolabs in agiprolabs/claude-trading-skills. It has 478 recorded installs.
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