Zara's mindshare analytics goes beyond just sentiment analysis, tracking not just what people say, but how narratives form and evolve in the space. Combining social metrics, on-chain data, and market psychology gives you a comprehensive view of market sentiment and trend formation.
Mindshare analytics flow (simplified):
1. Social Intelligence
class SocialAnalyzer:
"""Analyze social media activity and sentiment."""
async def analyze_social_metrics(self, token: str) -> SocialMetrics:
"""Generate comprehensive social analysis."""
# Gather data from multiple sources
twitter_data = await self.get_twitter_metrics(token)
telegram_data = await self.get_telegram_metrics(token)
discord_data = await self.get_discord_metrics(token)
# Analyze sentiment and trends
sentiment = await self.analyze_sentiment(
twitter_data, telegram_data, discord_data
)
# Track narrative formation
narrative = await self.track_narrative(sentiment)
return SocialMetrics(
sentiment=sentiment,
narrative=narrative,
engagement=self.calculate_engagement(),
influence_score=self.calculate_influence()
)
2. On-chain Analytics
class ChainAnalyzer:
"""Analyze on-chain metrics and wallet behavior."""
async def analyze_chain_activity(self, token: str) -> ChainMetrics:
"""Generate on-chain analysis."""
# Get wallet and transaction data
holders = await self.analyze_holders(token)
transactions = await self.analyze_transactions(token)
whale_activity = await self.track_whales(token)
return ChainMetrics(
holder_distribution=holders,
transaction_patterns=transactions,
whale_movements=whale_activity,
smart_money_score=self.calculate_smart_money_score()
)
# Get quick sentiment analysis
@zara sentiment ETH
> "Honey, let me read the room for ETH! 🎭
>
> Social Vibe: Bullish AF (pardon my French)
> - Twitter: Everyone's tweeting like it's 2021
> - Telegram: More active than my DMs
> - Discord: The bulls are running the show
>
> Sentiment Score: 8.5/10 (we love to see it!)
> 24h Change: +15% (serving green candle realness)
>
> My take: The crowd's more hyped than me at a
> sale! But remember what they say about
> extreme sentiment... 👀✨"
2. Detailed Mindshare Analysis
# Get detailed mindshare analysis
@zara mindshare ETH --detailed
> "Time for a deep dive into ETH's mindshare! 🧠
>
> Social Metrics:
> - Volume: 250k mentions (louder than my sass)
> - Sentiment: 8.5/10 (serving main character energy)
> - Key Influencers: Active and bullish
>
> On-chain Tea:
> - Whale Activity: Accumulation phase
> - Smart Money Flow: Net positive
> - Holder Distribution: Healthier than my diet
>
> Narrative Analysis:
> - Main Theme: 'ETH 2.0 Hype'
> - Sentiment Shift: +15% from last week
> - Trend Strength: Strong (like my opinions)
>
> Community Behavior:
> - FOMO Level: Rising (check those degen vibes)
> - Fear/Greed: Greed (68/100)
> - Social Engagement: Higher than my heels
>
> Recommendation:
> The vibes are immaculate, but watch that FOMO!
> Consider scaling in entries as sentiment might
> be getting a tad too spicy! 🌶️✨"
3. Trend Analysis
# Analyze trend formation
@zara trend ETH --social --market
> "Let's dissect these trends, bestie! 📈
>
> Market Trends:
> - Price: Uptrend (we love an ascending queen)
> - Volume: Increasing (thickness in all right places)
> - Momentum: Strong (serving lewks)
>
> Social Trends:
> - Mentions: Parabolic (my kind of party)
> - Sentiment: Bullish (but getting crowded)
> - Narrative: 'ETH 2.0 Season' trending
>
> Watch Out For:
> - Extreme sentiment (too much of a good thing)
> - FOMO entries (don't be that trader)
> - Narrative shifts (stay alert, stay alive)
>
> Keep it fierce but cautious! 💅✨"
Integration Examples
1. Trading Bot Integration
from zara.mindshare import SentimentAnalyzer
from zara.trends import TrendDetector
class SentimentTrader:
"""Trading bot using sentiment analysis."""
async def analyze_opportunity(self, token: str):
"""Analyze trading opportunity."""
sentiment = await self.sentiment.analyze(token)
trends = await self.trends.detect(token)
# Combine sentiment with technical analysis
if (sentiment.score > 7.0 and
trends.is_bullish and
not sentiment.is_overheated):
await self.generate_entry_signals(token)
2. Alert System Integration
class SentimentAlerts:
"""Alert system for sentiment shifts."""
async def monitor_sentiment(self, tokens: List[str]):
"""Monitor sentiment changes."""
for token in tokens:
old_sentiment = self.get_cached_sentiment(token)
new_sentiment = await self.get_current_sentiment(token)
if self.is_significant_change(
old_sentiment, new_sentiment
):
await self.send_alert(
token=token,
change=new_sentiment - old_sentiment,
analysis=self.generate_analysis()
)
