content-analysis

Structured text analysis extracts entities, key claims, sentiment, framing, and narrative elements from pasted content or a URL. It returns a multi-part analysis detailing not just what is said but how it is said, applicable to content review, journalism, policy analysis, and research tasks.

Narrative framing analysis detects dominant storylines, perspective, sentiment, and how claims are presented within text. You need it for content review, journalism, and policy analysis where understanding how information is framed matters beyond the surface content.

You can analyze text from either a URL or by pasting content directly. The tool requires one of these inputs to perform structured entity extraction, claim analysis, sentiment evaluation, and narrative identification.

Entity extraction identifies People, Organisations, Locations, Dates, Products, and Financial figures within the text. This structured extraction supports deeper insights for research, journalism, and policy analysis tasks.

Yes, content analysis applies directly to policy analysis and research tasks. It surfaces primary and supporting claims, detects sentiment and framing, and identifies narrative elements to provide deep textual understanding.

The best way to extract claims and sentiment is using structured text analysis, which surfaces primary and supporting claims alongside sentiment and framing. This provides a multi-part breakdown of the document's source structure and narrative elements.