Sentiment Scoring Methodology: How Scores Are Calculated

Sentiment scoring methodology is the framework that transforms raw text analysis into actionable numerical scores. StonkWhisper's Whisper Index uses a proprietary multi-stage scoring methodology that goes beyond simple positive/negative classification to capture the nuance and conviction of market sentiment across thousands of tickers simultaneously.

The scoring pipeline begins at the individual post level. Each text item (post, comment, tweet, article) receives three sub-scores: polarity (bullish/bearish/neutral on a -1 to +1 scale), intensity (how strongly is the sentiment expressed, from 0 to 1), and quality (how substantive is the analysis, from 0 to 1). These sub-scores are multiplied together to produce a weighted contribution — a strongly bullish, high-quality DD post contributes far more to the aggregate score than a casually positive emoji reaction.

Aggregation is where StonkWhisper's methodology diverges significantly from simpler approaches. Rather than averaging all scores equally, the system weights by source quality (Reddit DD posts > casual comments), author credibility (established accounts with accurate track records > new accounts), recency (recent sentiment weighted exponentially higher), and platform (multi-platform confirmation > single-platform signals). The resulting Whisper Index is a 0-100 score where 50 represents neutral, above 70 indicates strong bullish conviction, and above 90 represents extreme conviction.

The temporal dynamics of scoring are particularly important. StonkWhisper calculates both the current level (what does the crowd think right now?) and the derivative (is conviction increasing or decreasing?). A stock with a Whisper Index of 75 and rising carries a very different signal than one at 75 and falling. The platform's trending algorithms use this derivative analysis to identify momentum shifts before they're visible in the absolute score.

Validation of the scoring methodology is ongoing. StonkWhisper continuously backtests scoring parameters against actual price outcomes, adjusting weights to optimize signal quality. This feedback loop ensures that the Whisper Index remains predictive as market conditions and social media behavior evolve. The methodology is designed to adapt rather than remain static, which is critical in the rapidly changing landscape of social media-driven trading.