Full transparency into the production analysis layers that transform consumer reviews into sentiment scores, review momentum, signal context, snapshots, and exports.
Neural Core is the foundation of ReviewSignal's intelligence stack. It transforms raw review text into 384-dimensional semantic embeddings using MiniLM, then applies Isolation Forest anomaly detection to identify statistically significant deviations in rating, sentiment, and review volume across all tracked locations.
Echo Engine models how consumer sentiment cascades across geographic and brand networks. Using sparse matrix propagation and current confidence scoring, it generates review intelligence signals when sentiment at one location is supported by related locations and competing brands.
The pipeline starts with review text, ratings, timestamps, locations, and chain metadata. Public claims are tied to coverage counts and freshness labels so users can see whether a view is current, delayed, or historical before using it in research.
Cortex AI transforms quantitative signals into written analysis for the exact dashboard or report context. It uses section-specific prompts and a constrained institutional research voice rather than one generic market summary.
The final layer packages sentiment, momentum, anomaly, and propagation context into dashboard snapshots, downloadable reports, and machine-readable exports. The goal is review evidence that can move into an analyst workflow without overstating certainty.
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