social-media-intelligence

To extract financial sentiment signals from social media, this skill collects discussions from Twitter, Telegram, Discord, and Reddit, then applies VADER and FinBERT NLP models to score text and detect market extremes like fear and greed.

Yes, you can use FinBERT and VADER together for quantitative trading sentiment analysis. This skill employs both models alongside LLM-based analysis to quantify text sentiment and identify abnormal buzz preceding significant price volatility.

The best way to monitor r/wallstreetbets and Twitter cashtags for market signals is using this skill's multi-platform intelligence. It aggregates high-volume social media discussions to provide a unified view of real-time market sentiment shifts.

Yes, this sentiment analysis skill requires integration with platform-specific APIs for Telegram and Discord data collection. It leverages libraries like telethon and discord.py to fetch fragmented, high-volume financial discussions across multiple platforms.

You quantify fear and greed market extremes from social media discussions by applying the skill's sentiment scoring mechanisms. It uses transformers and LLM-based analysis on collected text to detect sentiment shifts that precede significant price volatility.

To run social media sentiment analysis for trading, you need Python dependencies including tweepy, ntscraper, telethon, discord.py, praw, vaderSentiment, transformers, torch, pandas, numpy, scipy, and scikit-learn.