social-media-intelligence

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.

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.

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.

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.

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.

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.