See Beyond the Noise.

Sentiment analysis applies language models to news articles, research notes, and public discussion to estimate how positive or negative the tone around an asset is at a given moment. It measures expressed mood — not value, and never a price forecast.

Used carefully, it supplies context that raw price data cannot: it can show that a single headline is being amplified far beyond its substance, or that a quiet narrative is building steadily without much coverage at all.

A single piece of news reprinted across forty outlets can feel like forty independent confirmations. Deduplicating coverage back to its original source is one of the highest-value steps in any market news analysis workflow.

The second step is stating, in advance, what evidence would contradict a developing market trend. Claims that cannot be contradicted are usually narratives rather than information — and they are the ones that spread fastest.

Cryptocurrency markets run continuously, generate an enormous volume of commentary, and lack a single authoritative news source. That makes them an ideal starting point for turning complex market information into clearer, more understandable intelligence.

The underlying components — data ingestion, news analysis, sentiment tracking, trend summarisation — are asset-agnostic. Adding equities or commodities later becomes a question of coverage rather than of rebuilding the platform.