What text analysis features does TextRazor provide?

Updated

TextRazor is a natural language processing API that extracts meaning from text, and its core capabilities are entity extraction, disambiguation and linking; keyphrase extraction; automatic topic tagging and classification; and deeper semantic analysis covering relations, typed dependencies and synonyms. It works across 19 languages and can classify content against the full IAB Content and IPTC Media Topic taxonomies. It is available as a cloud service or as self-hosted infrastructure, and you can evaluate it with a free API key and the online demo before committing to integration.

Core extraction features

Entity extraction, disambiguation and linking

TextRazor identifies the entities mentioned in your content and resolves which real-world thing each mention refers to, then links it to a knowledgebase entry. This matters when the same name could mean several things — a company, a person, a place — and you need the right one for downstream search, tagging or analytics.

Keyphrase extraction

The API pulls out the significant phrases in a document rather than just single words, giving you a compact summary of what the text is about. This is useful for indexing, summarization and content recommendation.

Automatic topic tagging and classification

TextRazor tags and classifies content automatically, and it supports the full IAB Content and IPTC Media Topic taxonomies. If your organization already uses those standard taxonomies for ad targeting, content management or media metadata, you can map results directly instead of building a custom category scheme.

Deeper semantic analysis

Beyond surface extraction, TextRazor performs deep analysis to surface:

  • Relations — connections between entities and concepts in the text
  • Typed dependencies — grammatical relationships between words that reveal sentence structure
  • Synonyms — alternative wordings that help with context-aware matching

These outputs support applications that need to understand context rather than just count keywords, such as semantic search, question answering and content enrichment.

What the demo shows

The site's demo uses a news passage about Barclays, Sheikh Mansour and Abu Dhabi investment to illustrate extraction. Running text like this through the API is a practical way to see how entities, keyphrases and topics come back before you write any code. The site also provides getting-started material, tutorials, and reference documentation for Python, PHP, Java and the REST API, plus a Custom Logic Engine for tailoring analysis.

Choosing between cloud and self-hosted

Consideration Cloud API Self-hosted
Setup effort Low — get an API key and call the endpoint Higher — you run the analysis infrastructure
Data location Text is sent to TextRazor's service Text stays in your environment
Best for Quick integration, prototypes, variable volume Strict data-handling requirements or high, steady volume

If your content cannot leave your infrastructure, the self-hosted option is the relevant one; otherwise the cloud API is the faster path. Check the pricing page for plan details and any limits before you decide, since the site lists pricing and login/sign-up as separate steps.

Practical next steps

  1. Sign up for a free API key and run a sample of your own text through the demo to confirm the output matches your needs.
  2. Pick the client reference for your stack (Python, PHP, Java or REST) and follow the getting-started tutorial.
  3. Test on a representative batch of your documents — especially ambiguous names and multi-language content — to see how disambiguation and the 19-language support perform.
  4. If you need custom rules, evaluate the Custom Logic Engine; if data residency is a constraint, confirm self-hosted deployment requirements.