How to Get Started with the TextRazor API

Updated

To start with TextRazor, sign up on textrazor.com to obtain a free API key, then use the demo to see the output before writing any code. From there, work through the Getting Started tutorial and pick the reference for your language (Python, PHP, Java, or REST). TextRazor is a natural language processing API that extracts entities, keyphrases, topics, relations, and typed dependencies from text in 19 languages, and it runs either in the cloud or self-hosted.

Step 1: Create an account and get an API key

The site's navigation includes Sign up and Login links alongside Pricing. The page states that a free API key is available, so the practical first step is to register and retrieve your key from the account area. Every API call you make will be authenticated with that key, so keep it out of public repositories.

Step 2: Try the demo before you build

The homepage offers a Try The Demo option and shows a sample news article about Barclays as example input. Running your own text through the demo is the fastest way to see what the API returns — which entities it links, which keyphrases it ranks, and how it tags topics — before you commit to an integration. Use a short paragraph of your own content rather than the sample, so the output reflects your actual use case.

Step 3: Choose your integration path

TextRazor documents several ways to call the API. Pick based on your stack:

Path Use it when
REST API Reference You're working in a language without an official library, or you want to see raw request/response structure
Python Reference Your pipeline is in Python
PHP Reference Your application runs on PHP
Java Reference You're integrating into a JVM service
Getting Started / Tutorials You want a guided first call end to end

The general flow is the same in each case: send text plus your API key to the service, and receive structured analysis back. Start with the Getting Started tutorial to confirm your key works, then move to your language reference for the details of request parameters and response fields.

Step 4: Decide which analyses you need

The API covers several distinct capabilities, and you don't have to use all of them:

  • Entity extraction, disambiguation, and linking — identifies people, organizations, and places in the text and links them to knowledgebase entries. In the sample article, this is what would resolve "Barclays," "Sheikh Mansour," and "Abu Dhabi" as distinct, linked entities.
  • Keyphrase extraction — surfaces the main phrases in a document.
  • Topic tagging and classification — assigns topics using the full IAB Content and IPTC Media Topic taxonomies.
  • Deeper analysis — relations, typed dependencies between words, and synonyms, for context-aware semantic applications.

If you only need to tag a stream of articles by subject, topic classification plus keyphrases may be enough. If you're building search or knowledge-graph features, entity linking and relations matter more.

Step 5: Plan for deployment and volume

TextRazor offers both a complete cloud service and self-hosted infrastructure. The cloud option is the default starting point; self-hosting becomes relevant if you have data-residency or throughput requirements that a hosted API can't meet. Pricing details are on the separate Pricing page — check it before you scale past experimentation, since the free key is intended for trying the API, and production volume terms are set out there.

Common sticking points

  • Skipping the demo. Teams often go straight to code and then struggle to interpret response fields. Ten minutes with the demo on your own text prevents that.
  • Assuming one language. The API supports 19 languages, but confirm yours is among them before designing around it.
  • Ignoring taxonomy fit. Classification maps to IAB and IPTC taxonomies; if your content doesn't fit those categories, plan to map the returned topics onto your own scheme.
  • Key handling. The key is tied to your account, so treat it as a secret from the first commit.