social-media-intelligence

Name: social-media-intelligence
Availability: InStock
Author: 0xzknw

Collect and analyze financial sentiment from social platforms for trading strategies.

Updated Jun 30, 2026

Search Tags:#social media #data collection #market intelligence #sentiment analysis #trading #finbert

Ready to run

npx skills add https://github.com/0xZKnw/vibe-trading-tap --skill social-media-intelligence-0xzknw

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: social-media-intelligence
Source: https://github.com/0xZKnw/vibe-trading-tap/tree/main/agent/src/skills/social-media-intelligence
Command: npx skills add https://github.com/0xZKnw/vibe-trading-tap --skill social-media-intelligence-0xzknw

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, high-volume social media discussions to identify actionable financial signals and sentiment trends.

Core Features & Use Cases

Multi-Platform Aggregation: Collects and normalizes data from Twitter/X, Telegram, Discord, and Reddit.
Sentiment Quantification: Provides multiple methods for sentiment scoring, including VADER, FinBERT, and LLM-based analysis.
Use Case: A trader can use this skill to monitor r/wallstreetbets and Twitter cashtags to detect abnormal buzz or sentiment shifts that precede market volatility.

Quick Start

Use the social-media-intelligence skill to fetch the latest sentiment analysis 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 trading?▼

Social media sentiment analysis for trading uses NLP models like FinBERT and VADER to score financial sentiment from platforms like Twitter and Reddit. It normalizes fragmented discussions into quantitative signals, enabling you to detect abnormal buzz or sentiment shifts that may precede market volatility.

Does this skill support collecting data from both Reddit and Discord?▼

Yes, this skill supports multi-platform data collection from Reddit and Discord, alongside Twitter and Telegram. It uses libraries like praw and discord.py to gather and normalize high-volume social media discussions into a unified dataset for analysis.

What is the best way to quantify social media buzz for a specific stock ticker?▼

The best way to quantify social media buzz for a stock ticker is to aggregate mentions across platforms like Twitter and Reddit, then calculate buzz metrics using the skill's built-in quantitative signal extraction. This approach detects abnormal mention volumes that often precede market volatility.

Can I use FinBERT and VADER together for sentiment scoring?▼

Yes, you can use FinBERT and VADER together for sentiment scoring. This skill provides multiple methods for sentiment quantification, allowing you to leverage both transformer-based financial sentiment analysis and VADER's lexicon-based scoring to validate sentiment-reversal detection across asset classes.

How do I monitor r/wallstreetbets and Twitter cashtags to detect market shifts?▼

To monitor r/wallstreetbets and Twitter cashtags, this skill uses ntscraper and tweepy to collect relevant posts. It then applies NLP processing to detect sentiment shifts and abnormal buzz, providing actionable quantitative signals that inform your trading strategies before market volatility occurs.

What Python libraries do I need for social media market intelligence?▼

For social media market intelligence, you need tweepy, ntscraper, telethon, discord.py, and praw for data collection. For analysis, you need transformers, torch, vaderSentiment for NLP, and pandas, numpy, scipy, scikit-learn for quantitative signal extraction and buzz metric calculation.