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IgorGanapolsky
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Sentiment Analyzer

Aggregate news, social, and market sentiment into composite scores for trading decisions.

38|9|Updated Oct 30, 2025
Search Tags:#nlp#news-analysis#social-media#sentiment#trading-signals
One-click install
npx skills add https://github.com/IgorGanapolsky/trading --skill sentiment-analyzer
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Sentiment Analyzer
Source: https://github.com/IgorGanapolsky/trading/tree/main/.claude/skills/sentiment_analyzer
Command: npx skills add https://github.com/IgorGanapolsky/trading --skill sentiment-analyzer

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires src/utils/news_sentiment.py, src/utils/sentiment_loader.py, src/utils/reddit_sentiment.py, src/rag/sentiment_store.py, and includes scripts (resource) components.

What problem does it solve?

This Skill aggregates sentiment from financial news and social platforms to inform trading decisions with a composite view.

Core Features & Use Cases

  • News sentiment: analyze financial news sentiment across sources.
  • Social sentiment: aggregate Twitter/Reddit/StockTwits sentiment.
  • Composite scores: generate confidence scores and trend signals.
  • Real-time monitoring: track sentiment dynamics.

Quick Start

Query sentiment aggregators for a list of symbols to obtain current sentiment scores.

Frequently Asked Questions about Sentiment Analyzer

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I aggregate sentiment from news and social media for trading signals?▼

Sentiment aggregation combines financial news and social platform signals into composite scores normalized to a -1 to +1 scale, weighted by source reliability, to generate trading-ready confidence metrics and trend indicators across multiple ticker symbols in real time.

Can I use social media sentiment analysis for stock market decisions?▼

Yes. This Skill extracts sentiment from Twitter, Reddit, and StockTwits alongside news sources, applies source-specific weighting, detects anomalies, and produces per-source breakdowns with timestamps to support informed trading workflows.

How do I monitor real-time sentiment dynamics across multiple data sources?▼

Real-time monitoring tracks sentiment changes by querying aggregators for ticker symbols, retrieving current composite scores, confidence levels, and trend signals updated continuously across news, social, and microstructure data sources.

What's the difference between single-source and multi-source sentiment for trading?▼

Multi-source sentiment normalizes signals from news, social platforms, and market microstructure with independent weights and confidence scoring, reducing false signals and producing more reliable composite scores than any single source alone.

Do I need preprocessing or normalization before applying sentiment analysis to trading data?▼

Yes. Input sentiment must normalize to a -1 to +1 scale, apply source-specific weights, map confidence levels, and structure outputs with timestamps and per-source breakdowns to ensure compatibility with trading workflows.

How does anomaly detection improve sentiment-based trading signals?▼

Anomaly detection identifies unusual sentiment spikes or drops across sources, flags deviations from baseline patterns, and surfaces high-confidence trend reversals to alert traders to potential market-moving events before they fully materialize.

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