Sentiment Analysis

Sentiment analysis assigns a direction and strength to text: is this article, filing, or post positive, negative, or neutral about its subject, and how strongly. Applied to a stream of news and social content, it converts unstructured language into a numeric series that can be tracked, aggregated by entity, and fed into a model.

General-purpose sentiment models tend to disappoint on financial text, where the vocabulary is domain-specific and the tone is often understated - "in line with expectations" is not neutral to a market. The sharper approaches are aspect-based, scoring sentiment toward a specific company or topic rather than the document as a whole, and are trained on finance language. Sarcasm, negation, and boilerplate remain the persistent failure modes, so the output is a signal to weigh, not a verdict to trust blindly.