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social-media-intelligence

Collect and analyze financial sentiment from social media platforms for trading decisions.

Updated Apr 10, 2026
Search Tags:#twitter#discord#reddit#social-media#telegram#sentiment-analysis#financial-signals
One-click install
npx skills add https://github.com/ebrahim-sani/trading-automation --skill social-media-intelligence-ebrahim-sani
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: social-media-intelligence
Source: https://github.com/ebrahim-sani/trading-automation/tree/main/vibe-trading/agent/src/skills/social-media-intelligence
Command: npx skills add https://github.com/ebrahim-sani/trading-automation --skill social-media-intelligence-ebrahim-sani

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Traders and analysts struggle to capture real‑time market sentiment from the flood of posts on Twitter/X, Telegram, Discord, and Reddit, missing valuable early signals that can inform trading decisions.

Core Features & Use Cases

  • Multi‑platform data collection – Retrieve messages, tweets, and channel posts across the four major financial social platforms.
  • Sentiment scoring – Apply VADER, FinBERT, or LLM models to generate sentiment, buzz, and fear‑greed indices.
  • Signal aggregation – Combine weighted platform scores into a single factor for back‑testing, factor research, or live trading alerts.
  • Use case – A quant fund can pull the latest Reddit posts about $GME, compute a bullish/bearish score, and trigger a short‑term momentum trade when extreme greed is detected.

Quick Start

Ask the social-media-intelligence skill to fetch the latest sentiment for AAPL from Twitter and summarize the top bullish and bearish points.

Frequently Asked Questions about social-media-intelligence

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

FAQPage Schema
How do I analyze social media sentiment for financial trading signals?▼

Social media sentiment analysis for trading signals aggregates posts from Twitter/X, Telegram, Discord, and Reddit, applying VADER, FinBERT, or LLM models to compute bullish or bearish scores for specified tickers.

What's the best way to track real-time market sentiment across Twitter and Reddit?▼

Tracking real-time market sentiment across these platforms involves collecting data streams via API credentials and generating standardized JSON outputs containing buzz metrics and fear-greed indices for live trading alerts.

Can I use FinBERT to calculate fear and greed indices from Discord and Telegram data?▼

Yes, FinBERT can calculate sentiment, buzz, and fear-greed indices from Discord and Telegram data by processing channel posts retrieved through platform-specific API credentials into aggregated weighted scores.

Do I need API credentials to collect financial signals from social media platforms?▼

Yes, API credentials for each platform are required to retrieve messages, tweets, and channel posts from Twitter/X, Telegram, Discord, and Reddit for financial signal collection and sentiment scoring.

How do VADER and LLM models compare for analyzing crypto sentiment on social media?▼

VADER provides rule-based sentiment scoring while LLM models offer context-aware analysis of crypto sentiment on social media, with both outputting standardized JSON scores suitable for factor research or back-testing.

How do I aggregate sentiment scores from multiple social platforms into a single trading factor?▼

Aggregating sentiment scores from multiple platforms combines weighted scores from Twitter/X, Telegram, Discord, and Reddit into a single factor, outputting standardized JSON for momentum trades or live alerts.

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