Detect Market-Moving Tweets Before the Market Does
Build a real-time crypto sentiment analysis system using Xpoz MCP and Claude to identify social signals that precede price movements—before they hit mainstream news.
The Problem
Crypto markets are uniquely sensitive to social sentiment. A single tweet from a key influencer can move billions in market cap within minutes. Traditional approaches leave traders at a disadvantage:
Information lag
By the time sentiment reaches news outlets, the move has already happened
Data fragmentation
Monitoring Twitter, Reddit, and other platforms manually is impossible at scale
Signal vs. noise
Millions of crypto-related posts daily make it hard to identify what actually matters
API complexity
Building custom scrapers for each platform requires maintaining multiple API integrations and handling rate limits
The Workflow
Building an effective crypto sentiment bot requires three components working together: data collection, intelligent analysis, and actionable output.
Example Queries
Ask Claude in natural language. Here are some examples with the underlying API calls:
Monitor influencer sentiment on a specific token
>What are the top crypto influencers saying about Solana in the last 24 hours? Show me their posts with engagement metrics.
Detect unusual activity patterns
>Find accounts that don't normally post about crypto but mentioned Bitcoin in the last 6 hours. Sort by follower count.
Track narrative emergence
>Count how many times 'ETH ETF' was mentioned each day over the past month. Compare to the previous month.
Analyze discussion sentiment
>Get the comments on this viral crypto tweet and categorize the sentiment. Are people agreeing or pushing back?
Why XPOZ
No platform API keys needed
Access Twitter and Reddit data without managing individual platform credentials or navigating their approval processes
Natural language interface
Query social data conversationally instead of writing code—Claude handles the translation to structured API calls
Unified data model
One consistent interface across platforms means you can compare Twitter and Reddit sentiment without normalizing different data formats
Built-in pagination and exports
Handle large datasets automatically, with CSV exports available for deeper analysis in your own tools
Authenticity signals
Twitter data includes bot detection scores, helping filter out coordinated inauthentic activity that could skew sentiment readings
Frequently Asked Questions
Xpoz focuses on social media intelligence. For on-chain data, you'd integrate with blockchain analytics tools. However, social signals often precede on-chain movements, making Xpoz valuable as an early warning system.
Use `getTwitterUserConnections` to map follower networks of accounts that historically preceded price movements. Cross-reference with engagement metrics on their crypto-related posts using the aggregation fields like `relevantTweetsLikesSum`.
Xpoz uses intelligent caching with automatic freshness checks. For time-sensitive monitoring, the `forceLatest` parameter bypasses cache for real-time data, though this increases API costs.
Yes. Every query that returns large datasets includes a `dataDumpExportOperationId` for CSV export. Download complete datasets to correlate historical sentiment with price action in your own analysis environment.
Twitter user data includes `isInauthentic` and `isInauthenticProbScore` fields. Filter your queries to exclude accounts flagged as potentially inauthentic, improving signal quality.
Get Started
Setting up your crypto sentiment system takes about two minutes:
Connect Xpoz MCP to Claude Desktop or Claude.ai via Settings → Connectors → Add custom connector → `https://mcp.xpoz.ai/mcp`
Authenticate with your Google account when prompted
Start querying with natural language—try "What are people saying about Bitcoin on Twitter right now?"
The free tier includes 100,000 results per month—enough to build and test your sentiment analysis workflow before scaling up.
Ready to Build Your Detect Market-Moving Tweets Before the Market Does?
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