Azure Cognitive Services - Create Text Analytics Service For Natural Language Processing

In this article, we’ll learn in step-by-step process to create the Text Analytics Service in the Azure Cognitive Services. The text analytics will come in handy while working with projects related to NLP ie. Natural Language Processing and provides features such as Sentiment Analysis, Text Summarization, Key Phrase Extraction and many more. This article is also a prerequisite for the article series on Sentiment Analysis. Let us get introduced to Azure Cognitive Services, Text Analytics and then go through the tutorial.

Azure Cognitive Services

Azure Cognitive Services is one of the services offered by Microsoft Azure that enables a wide range of organizations to build cognitive intelligence for applications with client library SDKs and REST APIs. Azure Cognitive Services allows developers to integrate cognitive features into applications with no prior knowledge of Machine Learning, Data Science, and Artificial Intelligence skillset. From Computer Vision to Natural Language Processing and Conversational AI, Azure Cognitive Services provides the support for a diverse prospect of applications.

Language Service

Texts can now be understood, analyzed, and even translated using the Language service in Azure Cognitive Services. The tedious mundane tasks of QnA now be automated with the simple conversation of the FAQ to the interactive chatbot. Sentiments can now be extracted and the key phrases can be used for text analytics all the while understanding the meaning of the comment of the user through the Language Understanding Service. The meaning from the unstructured text can be extracted with the services such as Language Understanding, QnA Maker, Text Analytics and Translator in Azure Cognitive Services (ACS).  The Text Analytics Service is now taken under the Language Service in Azure.

Text Analytics

The Text Analytics under Azure Cognitive Services enables extraction, classification and understanding of text in documents and is in fact a collection of features available for Language. From Sentiment Analysis, Summarization of Documents, Processing of Medical Text Data, Broad Entity Recognition, Text Analytics has the capability to support numerous scenarios.

Now, let us dive into the process to create the Text Analytics in Azure.

Step 1

Login to the Azure Portal. You’ll be taken to the Home Page.

Step 2

Now, Search for Cognitive Services in the Search Bar.

Also, you can Check in from the Menu on the top-left corner. Select the All Services Option.

You’ll be taken to the services list page. Here, select, AI + Machine Learning.

Now, Choose the Cognitive Services under to know about the services or straight away Language.

Step 3

Now, we can see the Language Service under Language. Above, you can see, the note that Text Analytics has now been rebranded and incorporated in Azure Cognitive Service for Language. This basically means that you can use the Text Analytics from the Language Service from now on.

Next, Click on Create under Language Service.

Step 4

You can see the list of default features. You can also use the Custom Features if you need. For now, straight away click on Continue to Create your resource.

Step 5

Now, fill in the details for Project and Instance. This service will compulsorily require paid subscription of Azure Sponsorship pass.

Here, on Subscription, I choose the Azure Pass – Sponsorship.

Next, fill in the Resource Group or Create a new one if you don’t have any resource group in Azure currently.

Select the Region, your name and Choose the Pricing Tier.  Once, all the details are filled, Click on Review + Create.

The validation test is run and once it is passed, a tick bar will acknowledge it.

Now, Click on Create.

Step 6

The Deployment process is now initialized. We’ll know about it from the notification bar and also from the Deployment progress page.

Step 7

Once, the deployment is successful, we’ll be provided with the button to Check out the resources from Go to Resource.

Step 8

Finally, we can see, we have created the ojashtextanalytics. This service can now be utilitized from numerous projects as per our need. We’ll later use this service for our Sentiment Analysis with Azure Synapse Analytics.

Conclusion

Thus, in this article, we learned about Azure Cognitive Services, Text Analytics and went through the step-by-step process to create a Text Analytics Service in Azure which is now rebranded as Language Service. This will now enable us to use the Natural Language Processing power of Azure for various projects.