social-media-intelligence

Extract financial sentiment signals from Twitter, Telegram, Discord, and Reddit.

Updated Jul 29, 2026

Search Tags:#finance #signals #trading #social-media #sentiment #quant

Ready to run

npx skills add https://github.com/santoosaraujo/vibe-trading-claude --skill social-media-intelligence-santoosaraujo

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: social-media-intelligence
Source: https://github.com/santoosaraujo/vibe-trading-claude/tree/main/.claude/skills/social-media-intelligence
Command: npx skills add https://github.com/santoosaraujo/vibe-trading-claude --skill social-media-intelligence-santoosaraujo

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires tweepy, ntscraper, telethon, discord.py, praw, vaderSentiment, transformers, torch, pandas, numpy, scipy, scikit-learn.

What problem does it solve?

This skill solves the challenge of monitoring fragmented social media platforms to identify actionable financial sentiment and market signals.

Core Features & Use Cases

Multi-Platform Collection: Aggregates data from Twitter, Telegram, Discord, and Reddit using specialized API wrappers.
Sentiment Quantification: Provides multiple scoring methods including VADER, FinBERT, and LLM-based analysis to gauge market mood.
Signal Generation: Calculates buzz metrics, fear-and-greed indices, and institutional vs. retail sentiment to support trading strategies.

Quick Start

Use the social-media-intelligence skill to fetch the latest sentiment data for the ticker AAPL from Twitter and Reddit.

Frequently Asked Questions about social-media-intelligence

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

FAQPage Schema

How do I extract financial sentiment signals from social media for quantitative trading?▼

You extract financial sentiment signals by aggregating data from Twitter, Telegram, Discord, and Reddit, then applying NLP models like FinBERT or VADER to quantify market mood and generate actionable metrics for quantitative trading strategies.

What social media platforms can I collect financial sentiment data from?▼

You can collect financial sentiment data from Twitter, Telegram, Discord, and Reddit. The process uses specialized API wrappers to aggregate fragmented social media posts and identify actionable market signals across these platforms.

Does social media sentiment analysis work with FinBERT and VADER for market mood quantification?▼

Yes, social media sentiment analysis supports FinBERT and VADER for market mood quantification. It provides multiple scoring methods, including LLM-based analysis, to calculate buzz metrics and fear-and-greed indices.

How do I calculate a fear-and-greed index from Twitter and Reddit posts?▼

You calculate a fear-and-greed index by extracting financial sentiment data from Twitter and Reddit posts, then applying sentiment classification models to gauge market mood and generate institutional vs. retail sentiment scores.

Can I generate buzz metrics and author-weighted sentiment scores for specific stock tickers?▼

Yes, you can generate buzz metrics and author-weighted sentiment scores for specific stock tickers. The system fetches the latest sentiment data and quantifies signals to support targeted trading strategies.

What are the limitations of using NLP models for social media sentiment in trading strategies?▼

Limitations of using NLP models for social media sentiment include the need for integration with platform-specific APIs and dependencies on libraries like transformers and torch, requiring advanced setup to process fragmented social data accurately.