social-media-intelligence

To extract financial signals for trading strategies, this Skill gathers social media data from Twitter, Telegram, Discord, and Reddit, then applies sentiment analysis using VADER and FinBERT to compute actionable metrics like buzz scores and fear/greed indicators.

Yes, you can analyze Twitter cashtag sentiment and Reddit discussion buzz metrics together, as the Skill collects data across multiple platforms and computes unified metrics like message volume, unique authors, and engagement scores.

You need Python libraries including tweepy, ntscraper, telethon, discord.py, and praw for social media API access, alongside vaderSentiment and transformers for processing sentiment analysis and computing financial signals.

Sentiment extremes and fear/greed indicators detect market reversal signals by calculating quantitative shifts in social media sentiment, allowing you to identify when market emotions reach unsustainable levels and anticipate directional changes.

VADER and FinBERT both analyze financial signals from social media, but FinBERT via the transformers library offers specialized financial context understanding, whereas VADER provides general sentiment scoring for computing baseline fear/greed indicators.

The best way to track real-time sentiment shifts for earnings reports is using platform-specific analysis to monitor Twitter KOL influence and calculate discussion-buzz metrics, enabling informed sentiment-driven trading decisions.