Text Analytics Api Overview

uClassify is an open tool to allow users to create and share classifiers. Todo this uClassify provides three APIs (URL, XML and Local Server), here is a short overview and comparison of those.


The URL API is simple to use, just send an GET request with an URL to get a response.

For example making a sentiment classification (sign up for a free read api key):

The response will be in XML or alternatively JSON. You can classify a text or let uClassify download an URL for you and classify the content (optionally with or without the HTML).

When to use the URL API
– Want to get up and running quickly
– Low volumes of classifications (can be slow on many requests at once)
– Each text is only run through one classifier (otherwise you should batch with XML API)
– If you only is interested in classifying and not training or other fancy features.

The URL API documentation can be found here.


With the XML API you can do everything. You can create classifiers, train them, classify, extract keywords and more. Another important feature is that you can batch calls. This means that you can get thousands of, say, tweets or blog posts, classified in one single request as long as the request string is less than 1 MB.

To make an XML request, you simply build an XML string and POST it to:
http://api.uclassify.com/ on port 80.

Here is how you batch two calls into one request:

<?XML version="1.0" encoding="utf-8" ?>
<uclassify xmlns="http://api.uclassify.com/1/RequestSchema" version="1.01">
    <textBase64 id="UnknownFantasyText1">SXcgYklRdHFEYXEgYklsZW5namFq</textBase64>
    <textBase64 id="UnknownFantasyText2">UGFkb2wgcmFpZCwgYXRoYW4gaGVuZGFk</textBase64>
  <readCalls readAPIKey="YOUR_READ_API_KEY_HERE">
    <classify id="Classify1" classifierName="FantasyLanguage" textId="UnknownFantasyText1"/>
    <classify id="Classify2" classifierName="FantasyLanguage" textId="UnknownFantasyText2"/>

When to use the XML API
– Gain performance by batching multiple calls into one request
– Need access to the full API (classifier creation, training, keywords etc)
– This is the recommended way

You can find the complete documentation here.

Local Server

As a final option you can run a local classification server. This is for users who have huge amounts of data to process.

The API is pretty much the same as the XML API except that no authentication is needed.

When to use a local classification server
– You have huge amounts of data to process
– CPU performance is very important (you don’t have to share CPU cycles with other uClassify users)
– Install the server in your own data centre, to avoid any network lag to uclassify.com
– Have full control over the server

You can find more info here and the technical server manual.

Tutorial – Creating your own classifier

This is a brief tutorial of how to create your own classifier. I’ve used the term class synonymously to category and classifier to categorizer.

1. Determine the classifier domain

Before a classifier can start to classify it needs to be created and trained. First you should ask yourself what you want the classifier to do, is it a spam filter? a news categorizer? Let’s assume it’s a news categorizer for this tutorial. So we create a news classifier with the name ‘Example News Categorizer’.

Fig 1. Create the classifier

2. Define the relevant classes

Secondly you need define what classes your classifier should include. Choosing relevant classes is straightforward – just ask yourself what categories are relevant for the domain you have chosen. Once you have selected the classes you want the classifier to distinguish between you create them. This is easy in our Graphical User Interface but can also be done via our web API. For our small example we create the following three classes: Science, Sports and Entertainment. You can create as many classes as you want.

Fig 2. Create the classes (categories)

You can also add and remove classes dynamically – so don’t worry if you aren’t 100% sure that you have included all.

3. Collect training data

Before the classifier can start to categorize texts into the classes we need to learn it how texts belonging to the different classes look. This is the hardest part as it requires you to collect actual training data. You can collect it from any source you find appropriate.

3.1 Amount of training data

It’s hard to generalize the amount texts needed for a classifier to work as it’s highly dependent on the domain. Simple domains such as classifying the language of a text only requires a small amount while harder problems such as seeing difference between texts written by males and females requires much more training data. However to test an idea I suggest at least 20 documents per category. With each document in the same format of those that will be used for classification later (e.g. for a spam filter you train it on e-mails). 20 is the bare minimum – from there the classifier only gets more accurate.

For our news categorizer I collected 20 plain text articles per class from random sources on Internet.

3.2 Automate the collecting!

In some cases you can automate the data collection by finding trusted sources on Internet. For example for our news classifier I could jack into three RSS feeds for Science, Sports and Entertainment and automatically gather the data. Ahhh, no manual collecting!! Nice.

4. Train the classifier

So you have collected training data in some form (perhaps text files on your hard drive or lists of urls or some feeds), now it’s time to train the classifier. This can be done manually in the GUI or automated if you have some basic programming skills. For our tutorial I found 20 news articles per class and copied and pasted the them manually into the GUI, it took me about 30 minutes.

Screenshot of training

Fig 3. Training the classifier via the GUI

4.1 Automate the training! (requires novice programming skills)

Training a classifier through the GUI can be cumbersome if large amounts of training data is tractable. My suggestion is to create a small script in your favorite language that automatically trains the classifier. If your training data is laying around on your machine locally (perhaps automatically collected?=) you can just batch it into our web API. If you haven’t collected the training data yet you could create a script that automatically collects it and train the classifier with it!

4. Start classifying

This is the fun part, when you have created your classifier you can start to use it. You can always test it in our GUI. Further you can (and should) build your own web site around it via our web API – providing the world with more semantics and cool classifications that never have been seen before! Also – remember that you can use your classifiers commercially and make money on it!

I’ve published the example classifier, don’t expect it to work perfectly – it has only been trained on 20 articles per class! Test it here – Example News Categorizer


  • Find out what you want to classify on and create a classifier
  • Define and create the categories
  • Collect training data for each category
  • Train each category on the gathered data
  • Build a really cool web site around it!