social-media-intelligence

Multi-platform social media sentiment data is extracted from Twitter, Telegram, Discord, and Reddit feeds. It applies VADER, FinBERT, and LLM scoring alongside buzz metrics to quantify fear-and-greed extremes and deliver structured sentiment collections for trading signal inference.

Telegram and Discord community chatter is turned into a quantifiable fear-and-greed index by applying weighted sentiment aggregation. The skill collects raw text feeds and applies VADER, FinBERT, and LLM scoring to compute weighted platform factors for extreme reversal cues.

Yes, you can backtest trading signals using multi-platform social media sentiment. The skill computes sentiment reversals, IC, and ICIR metrics from aggregated platform weights to test alpha and trigger alerts on extreme greed or fear.

The best way to monitor crypto community buzz involves harvesting Discord and Telegram feeds with compliance guardrails. This skill applies FinBERT and LLM scoring to quantify community activity, delivering structured collections that highlight sentiment-driven alpha and reversal cues.

Yes, the sentiment analysis supports VADER, FinBERT, and LLM scoring for earnings alerts. It harvests Twitter, Telegram, Discord, and Reddit feeds with suggested monitoring cadences for earnings and whale alerts to deliver weighted sentiment factors.