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Monkey Learn + Qlik Sense Integrations

Appy Pie Connect allows you to automate multiple workflows between Monkey Learn and Qlik Sense

About Monkey Learn

Create new value from your data. Train custom machine learning models to get topic, sentiment, intent, keywords and more.

About Qlik Sense

Qlik Sense is a modern data analytics platform. Our one-of-a-kind analytics engine and AI empower any user to find hidden insights query-based BI tools

Qlik Sense Integrations
Connect Monkey Learn + Qlik Sense in easier way

It's easy to connect Monkey Learn + Qlik Sense without coding knowledge. Start creating your own business flow.

    Triggers
  • New Collection

    Triggers when a new collection is created

  • New Space

    Triggers when a new space is created

  • New User

    Triggers when a new user is created

    Actions
  • Classify Text

    Classifies texts with a given classifier.

  • Extract Text

    Extracts information from texts with a given extractor.

  • Upload training Data

    Uploads data to a classifier.

  • Create App

    Creates a new app

  • Create Space

    Creates a Space

  • Create User

    Creates a user in a given tenant

  • Creates Collection

    Creates a new collection

  • Update Space

    Updates a space

  • Updates Collection

    Updates a collection

How Monkey Learn & Qlik Sense Integrations Work

  1. Step 1: Choose Monkey Learn as a trigger app and authenticate it on Appy Pie Connect.

    (30 seconds)

  2. Step 2: Select "Trigger" from the Triggers List.

    (10 seconds)

  3. Step 3: Pick Qlik Sense as an action app and authenticate.

    (30 seconds)

  4. Step 4: Select a resulting action from the Action List.

    (10 seconds)

  5. Step 5: Select the data you want to send from Monkey Learn to Qlik Sense.

    (2 minutes)

  6. Your Connect is ready! It's time to start enjoying the benefits of workflow automation.

Integration of Monkey Learn and Qlik Sense

Monkey Learn

MonkeyLearn is a service that lets you build text classifiers with machine learning, without having to deal with any algorithms, training, tuning or structured data.

MonkeyLearn provides general-purpose NLP tasks like text classification and sentiment analysis, as well as custom tasks for specific business needs.

The main categories of NLP tasks are sentiment analysis, text classification, entity extraction, keyword extraction, date extraction, name matching, language detection, location detection, relation extraction, dictionary creation and image captioning.

You can find more information about the MonkeyLearn API at http://monkeylearn.com/docs/api/.

Qlik Sense

Qlik Sense is a data discovery and exploration platform. It is used by visualizing large vpumes of big data in interactive self-service dashboards and reports. Qlik Sense is a top that offers on-demand sharing and cplaboration between users on different devices such as smartphones or tablets on top of an on-premise or cloud-based deployment model.

Qlik Sense is used by hundreds of companies around the world and has been implemented in many different sectors including retail, banking, insurance, energy and others.

You can find more information about the Qlik Sense platform at https://www.qlik.com/products/sense/.

Integration of Monkey Learn and Qlik Sense

We decided to integrate Monkey Learn and Qlik Sense because we wanted to create a simple top that would allow our own customers do sentiment analysis of their documents using Qlik Sense and get more insights from their data using Monkey Learn’s APIs for natural language processing (NLP. and machine learning.

While considering this integration we had to decide how we were going to use each service:

– We had to decide how we were going to send documents to Monkey Learn and get the results back into Qlik Sense. This was probably the most challenging part of the integration because we had to select the right document type for each task. We decided to use .docx files from Google Drive for document classification and .csv files from Dropbox for sentiment analysis. By doing this we could make sure that both services will understand the format with no issues. We also made sure that the documents were clean before sending them over to the services. We used a modified version of a Python script from this GitHub repository to clean the documents. This modified version didn’t remove all punctuation marks in a document but it stripped out most of them which was good enough for our use case. To be able to get the results from Monkey Learn back into Qlik Sense we used its REST API which allowed us to get the JSON objects from the classification requests back using HTTP GET request. For example. http://api.monkeylearn.com/v2/classify/document/file_pathtoken=<your token>&file_name=<filename>&type=<type>&text=<text>&modelid=<your model id>.json This request will return a JSON object with a response status code 201 if the classification was successful and a response status code 400 if there was an error while processing your request. The body of this response will be something like this. {“response_code”:”201″, “response_message”:”Successfully classified!”} If you want to get the confidence score for each class then you should add &confidence=true parameter after &modelid=<your model id>. If you want to get the response code (200/400. instead of response message (“Successfully classified!”/”Error while classifying!”. then you should add &response_code=true parameter after &modelid=<your model id>. The object below describes all possible options that can be used in this request. You can find more information about these parameters here. https://monkeylearn.com/docs/api/classify/#document-classification-parameters The next step was to use Qlik Sense’s HTTP Request Action which allowed us to send POST requests to other URLs through HTTP Request action in Qlik Sense. We used POST method because we wanted to get the JSON objects back from our request so we couldn’t use GET method which doesn’t allow us to send any query string parameters when making requests. As you may already know Qlik Sense allows us to create HTTP Request actions in our applications that will allow us to send GET or POST requests to any URL by filling out some parameters in the HTTP Request action in Qlik Sense. This option is available when you are connected to Qlik Sense Server through RDP or by opening QMC in your web browser. You can find more information about the HTTP Request action here. https://www.qlik.com/support/editors-and-aftersales/developers-guides/html5-helpers/http-request-action After creating an HTTP Request action in Qlik Sense we had to fill out its parameters. You can find this field under “Advanced Settings” section when editing an HTTP Request action in Qlik Sense Server. The parameters are described below. Function Name – The name of our function that would handle the request coming from an HTTP Request action in Qlik Sense. In our case this was “ClassifyDocs” which would handle requests coming from an HTTP Request action in Qlik Sense.

– The name of our function that would handle the request coming from an HTTP Request action in Qlik Sense. In our case this was “ClassifyDocs” which would handle requests coming from an HTTP Request action in Qlik Sense. URL – The URL where we want our POST requests sent to from an HTTP Request action in Qlik Sense. In our case this was “https://api.monkeylearn.com/v2/classify/document/token=<your token>&file_name=<filename>&type=<type>&text=<text>&modelid=<your model id>.json" so every request from an HTTP Request action would be sent to Monkey Learn’s API endpoint /v2/classify/document/token=<your token>&file_name=<filename>&type=<type>&text=<text>&modelid=<your model id>.json with POST method and JSON content type.

so every request from an HTTP Request action would be sent to Monkey Learn’s API endpoint /v2/classify/document/token=<your token>&file_name=<filename>&type=<type>&text=<text>&modelid=<your model id>.json with POST method and JSON content type. Content Type – The content type of the request sent from an HTTP Request action in Qlik Sense and JSON was selected as default value here so we didn’t have to change anything here.

– The content type of the request sent from an HTTP Request action in Qlik Sense and JSON was selected as default value here so we didn’t have to change anything here. Post Data – The data associated with the POST request sent from an HTTP Request action in Qlik Sense and we had separated each parameter with a comma so it would look like this. “token=<your token>,file_name=<filename>,type=<type>,text=<text>,modelid=<your model id>". Make sure that you replace <your token>, <filename>, <type>, <text>, <your model id> with your own values here otherwise MonkeyLearn won’t process your request correctly and you will get an error like “This request appears not to be properly formatted." Error Code . 400 if something is wrong with your request URL (e.g., incorrect protocp (http instead of https), missing parameters or invalid data. Error Message . “Bad Request - Invalid url format. Make sure it is valid and correct." Error Code . 400 if there was an error while parsing your request URL or you have bad credentials setup for connection with target url. Error Message . “Bad Request - Invalid credentials setup for target url." Error Code . 404 if target url does not exist or you don’t have access rights for this target url. Error Message . “Bad Request - Target url is not accessible." Error Code . 401 if there are not enough permissions for creating classifiers or creating models have failed due to insufficient permissions for one of these operations on target url

The process to integrate Monkey Learn and Qlik Sense may seem complicated and intimidating. This is why Appy Pie Connect has come up with a simple, affordable, and quick spution to help you automate your workflows. Click on the button below to begin.