social-media-intelligence

To collect social media sentiment for finance tickers, you provide an asset ticker and time window to aggregate cross-platform social data using platform-specific collectors. The Skill standardizes schemas across Twitter/X, Telegram, Discord, and Reddit for real-time or batch sentiment analysis.

You can backtest social media sentiment factors against forward returns by building sentiment-backed factors from collected data. The Skill correlates these aggregated sentiment scores with asset price movements over specified time windows.

This sentiment analysis tool supports event-driven monitoring around earnings or macro releases. It applies real-time data collection and weighted sentiment aggregation across Twitter/X, Telegram, Discord, and Reddit during these specific financial events.

Running multiplatform social media data collection requires Python dependencies including tweepy, ntscraper, telethon, discord.py, and praw. Additional dependencies like pandas, numpy, transformers, torch, vaderSentiment, and scikit-learn handle sentiment scoring and backtesting.

Privacy safeguards are implemented when scraping social signals from Discord, Telegram, Twitter, and Reddit. These protections are integrated into the platform-specific data collectors to ensure compliant data aggregation and sentiment analysis.