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stocksight-sentiment

Correlate Twitter and news sentiment with stock price movements using NLP pipelines.

10|2|Updated Mar 8, 2026
Search Tags:#twitter#nlp#trading#elasticsearch#stock#sentiment#kibana
One-click install
npx skills add https://github.com/mahmoud20138/Tradecraft --skill stocksight-sentiment
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: stocksight-sentiment
Source: https://github.com/mahmoud20138/Tradecraft/tree/main/plugins/tradecraft/skills/stocksight-sentiment
Command: npx skills add https://github.com/mahmoud20138/Tradecraft --skill stocksight-sentiment

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

StockSight provides an open-source pipeline to correlate social sentiment from Twitter and news headlines with stock price movements, enabling traders to quantify market mood and improve decision making.

Core Features & Use Cases

  • Collect stock-focused Twitter sentiment and news sentiment data
  • NLP processing with NLTK, TextBlob, and VADER for sentiment scoring
  • Elasticsearch storage and Kibana dashboards for exploration and monitoring
  • Docker or local install for flexible deployments
  • Use case: monitor a ticker like TSLA and compare sentiment trends with price moves to generate alerts

Quick Start

Install Stocksight and run it to begin collecting sentiment for a stock ticker.

Frequently Asked Questions about stocksight-sentiment

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

FAQPage Schema
How do I correlate Twitter sentiment with stock price movements?▼

Stock sentiment analysis uses NLP pipelines like NLTK, TextBlob, and VADER to score social media and news text, correlating those scores against ticker price movements to generate actionable trading signals.

Can I use Elasticsearch and Kibana to monitor stock sentiment dashboards?▼

Yes, you can use Elasticsearch and Kibana to monitor stock sentiment dashboards. The pipeline stores scored sentiment data in Elasticsearch and uses Kibana to visualize real-time monitoring and historical analysis across multiple tickers.

What is the best way to apply NLP sentiment scoring to real-time trading analysis?▼

Applying NLP sentiment scoring to real-time trading analysis involves ingesting tweets and news headlines, scoring text with VADER or TextBlob, and visualizing trends alongside price moves to generate actionable trading signals.

Does VADER work for analyzing financial news headlines for trading signals?▼

VADER works for analyzing financial news headlines by generating sentiment scores that can be correlated with stock price movements, enriching trading analysis and quantifying market mood across multiple tickers.

How do I set up historical sentiment analysis for multiple stock tickers?▼

Setting up historical sentiment analysis for multiple stock tickers requires collecting past tweets and news, processing text with NLP models, and storing scored data in Elasticsearch for historical querying and Kibana visualization.

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