Get Data from text using Machine Learning

Extract and classify information from text. Integrate with your App within minutes.


  • Easy to use

    No NLP or Machine Learning knowledge is required. Just play with our elegant UI and our Patent Pending Algorithm creation Engine.

  • Customized for your needs

    Pick a pre-trained module or create your own text mining module from scratch. Upload your text data and train your custom machine learning model.

  • Instant Integration

    Integrate with your project within minutes, MonkeyLearn is compatible with all major programming languages.

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Simple and Modern User Interface

Taste the power of Machine Learning in your hands!

Interface image
  • This is a Category Tree
  • These are your project statistics
  • These are your project samples
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How it works

Use MonkeyLearn in 3 simple steps!

  • Select or Create a Module

    1. Select or Create a Module

    You can select ready to use modules or create your own with a simple and elegant UX!

  • Test

    2. Test

    Use our web interface to test your module and improve the results.

  • Integrate

    3. Integrate

    Integrate with any programming language through an API that will be instantly published.

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Use Cases

  • MonkeyLearn is a match made in heaven for advertising companies.

    MonkeyLearn is a match made in heaven for advertising companies.

    Examples Ad Classifier: show ads in the optimal context and improve CTR. Affinity Profiler: classify users into affinity groups and match users with ads to increase ad revenue. In-market audiences: connect with those users most interested in what you have to offer and improve campaigns ROI.
  • MonkeyLearn is a match made in heaven for advertising companies.

    Machine learning is the future of media, newspapers and blogging.

    Examples News Classification: unify classification criteria and automate news categorization. Content Personalization: show readers relevant content that they are likely interested. News Recommendation: while reading an article, recommend more articles related to his personalized interests.
  • MonkeyLearn is a match made in heaven for advertising companies.

    MonkeyLearn allows developers and startups understand and process social media data.

    Examples Sentiment Analysis: understand if a tweet is talking positively or negatively about a brand or user. Follower Profiling: classify your Twitter followers by their interests. Profanity & Abuse Detection: detect bullying on social networks.
  • MonkeyLearn is a match made in heaven for advertising companies.

    Boost sales by integrating Machine Learning to your ecommerce.

    Examples Product Recommendation: show your customers relevant products they are likely interested in. Customer Profiling: classify customer profiles by what they tweet and use it for personalizing your email campaigns. Product Classification: classify products by their description.

Check out MonkeyLearn 101 applications and use cases here

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Read what people are saying about MonkeyLearn

  • "Adding Natural Language Processing should be a best practice but very unapproachable; MonkeyLearn changes the game."

    Jack Dorsey

    Eric Stone

    Founder at Telesocial.
    Entrepreneur & Hacker.
    San Francisco Bay Area

  • "I see a remarkable opportunity to integrate MonkeyLearn text analytics technologies into the digital Advertisement space."

    Jack Dorsey

    Santiago Pehar

    Vice President of Product, Batanga Media.
    Angel Investor & Advisor.

  • “Integrating Monkeylearn took 3 lines of code. Implementing similar functionality would take months; instead, we could keep things lean.”

    Jack Dorsey

    Alan Nichol

    Founder at TreevApp.
    PhD Student in Machine Learning
    Cambridge, UK.

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Super Easy Integration

Libraries & Documentation are provided to easily integrate MonkeyLearn with your favorite programming language.

  • Python

    import unirest
    response ="",
    		"Authorization": "token <YOUR TOKEN GOES HERE>"
    		"text": "This is a text to try language detection."
    print response.code
    print response.headers
    print response.body
  • Ruby

    require 'unirest'
    response = Unirest::post(
    	headers: {
    		"Authorization" => "token <YOUR TOKEN GOES HERE>"
    	parameters: {
    		:text => "This is a text to try language detection."
    puts response.code
    puts response.headers
    puts response.body
  • PHP

    require_once 'unirest-php/lib/Unirest.php';
    $response = Unirest::post(
    		"Authorization" => "token <YOUR TOKEN GOES HERE>"
    		"text" => "This is a text to try language detection."
  • JavaScript

    <!DOCTYPE html>
    <meta charset="UTF-8">
    <title>MonkeyLearn Javascript Integration</title>
    <script src="js/jquery-1.9.1.min.js"></script>
    <script type="text/javascript">
    	url : "",
    	type : "POST",
    	headers: {
    		"Authorization": "token <YOUR TOKEN GOES HERE>",
    	data : {
    		text: "This is a text to try language detection."
    	success : function(result) {
    	error : function(e) {
    		alert('Error: ' + e);
  • Java

    package com.monkeylearn;
    import com.mashape.unirest.http.Unirest;
    import com.mashape.unirest.http.HttpResponse;
    import com.mashape.unirest.http.JsonNode;
    import com.mashape.unirest.http.exceptions.UnirestException;
    public class App
    	public static void main( String[] args ) throws UnirestException
    		HttpResponse<JsonNode> response =
    		.header("Authorization", "token <YOUR TOKEN GOES HERE>")
    		.field("text", "This is a text to try language detection.")
  • .NET

    using System;
    using System.Collections.Generic;
    using System.Linq;
    using System.Text;
    using System.Threading.Tasks;
    using System.Net.Http;
    using System.Net.Http.Headers;
    namespace dotnet_integration
    	class Program
    		static void Main(string[] args)
    			using (var client = new HttpClient())
    				client.BaseAddress = new Uri("");
    				client.DefaultRequestHeaders.Authorization = new AuthenticationHeaderValue("token", "<YOUR TOKEN GOES HERE>");
    				var content = new FormUrlEncodedContent(new[]
    					new KeyValuePair<string, string>("text", "This is a text to try language detection.")
    				var response = client.PostAsync("api/v1/categorizer/hDDngsX8/classify_text/", content).Result;
    				if (response.IsSuccessStatusCode)
    					var responseBody = response.Content.ReadAsStringAsync().Result;
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