Get started with text analysis in Microsoft Foundry

In this exercise, you’ll use Microsoft Foundry, Microsoft’s platform for creating AI applications, to explore common text analysis techniques.

Foundry offers two approaches to text analysis: general-purpose AI models that handle a broad range of tasks through natural language prompts, and purpose-built language tools that return structured, deterministic results for specific tasks. By exploring both, you’ll gain a clearer understanding of when to use each approach.

In the first part of this exercise, you’ll use a general purpose AI model in the Foundry portal’s chat playground. In the second part of this exercise, you’ll explore some features of Azure Language in Foundry tools.

This exercise takes approximately 20 minutes.

Create a project in Microsoft Foundry

  1. In a web browser, open Microsoft Foundry at https://ai.azure.com to start building; signing in using your Azure credentials.

  2. If it isn’t already enabled, in the tool bar the top of the page, enable the New Foundry option. Then, if prompted, create a new project with a unique name; expanding the Advanced options area to specify the following settings for your project:
    • Foundry resource: Enter a valid name for your AI Foundry resource.
    • Subscription: Your Azure subscription
    • Resource group: Create or select a resource group
    • Region: Select any of the AI Foundry recommended regions in this list

    Note: Depending on your permissions in the Azure subscription, you may need to clear the option to set up recommended resources.

  3. Select Create. Wait for your project to be created. It may take a few minutes. After creating or selecting a project in the new Foundry portal, it should open in a page similar to the following image:

    Screenshot of the Foundry project home page.

    Note: Close any quick start panes in order to access your project’s Foundry home page.

Explore a general-purpose AI model’s text analysis capabilities

In this part of the exercise, you’ll use a general-purpose language model to perform text analysis through natural language prompts. A language model can handle a wide variety of tasks through prompting alone.

  1. Now you’re ready to explore models. On the Discover page, select the Models tab to view the Microsoft Foundry model catalog.

    Screenshot of the AI Foundry model catalog.

  2. Search for and select the gpt-5-mini model, and view the page for this model, which describes its features and capabilities.

    Screenshot of the gpt-5-mini model page with the default settings deployment option highlighted.

  3. Use the Deploy button to deploy the model using the default settings. Wait for the deployment to complete. After the deployment is complete, you’re taken to a chat playground, where you can test out the model’s capabilities.

Analyze sentiment

Sentiment analysis is a common natural language processing (NLP) task. It’s used to determine whether text conveys a positive, neutral or negative sentiment; which makes it useful for categorizing reviews, social media posts, and other subjective documents.

  1. In the chat playground page, use the button at the bottom of the left navigation pane to hide it and give yourself more room to work with.

  2. In the pane on the left, change the default Instructions to:

    You are an AI assistant that analyzes and summarizes text.

  3. In the Chat pane, the following prompt (you can press CTRL+ENTER for a new line):

    Summarize this review as a single, short paragraph:

This AI training course provides a clear and engaging introduction to core concepts such as machine learning, neural networks, and generative AI, making it accessible even to learners with limited prior experience. The course consistently reinforces key ideas through practical examples and hands-on exercises, which helps learners build confidence while applying AI techniques in real-world scenarios.
    
Another strength is the emphasis on modern tools and workflows, including prompt design and model evaluation, which are highly relevant for current industry needs. The instructors communicate complex topics in a simple, structured way, and the course materials are well organized to support progressive learning. I particularly appreciated how the course revisits important themes like model accuracy, responsible AI, and iterative improvement across multiple modules, reinforcing their importance.
    
Overall, this course offers a highly practical and well-rounded learning experience for anyone looking to develop foundational and applied skills in AI.

    The model should generate a summary of the text.

    Screenshot of text summarization results in the chat playground.

    Large language models (LLMs) are built on machine learning techniques that have their origins in natural language processing and text analysis, so they’re good at summarizing text, extracting named entities (such as people and place names), and classifying documents based on sentiment, topic, style, and other factors.

Use a specialized language analysis tool

While a language model that’s trained for general generative AI workloads can often do a great job of text analysis, sometimes a more specialized tool can be used by an agent to get more predictable results.

The Azure Language in Foundry Tools provides purpose-built analyzers that use statistical techniques to return structured, deterministic results—ideal for consistent output in automated pipelines.

  1. In the Foundry portal, navigate to the menu at the top of the screen and select Build.

  2. On the Build page, navigate to the menu on the left-side of the screen (you may need to expand it). In the menu, select Deployments. Then, at the top of the Deployments page, select AI Services.

    Microsoft Foundry Tools includes multiple AI Services (formerly known as Microsoft Cognitive Services) that support common speech, translation, language, and content understanding workloads.

    Screenshot of Foundry AI services page.

Detect language

In scenarios where text could potentially be in one of multiple languages, the first step in an analysis workflow is often to determine the primary language so the text can be routed to the most appropriate model or agent for the subsequent processing.

  1. In the list of AI services, select the Azure Language - Language detection analyzer.

  2. In the Input text list, select one of the provided sample documents. Then use the Detect button to detect the language in which the sample is written.

    Screenshot of a detected language in the Playground

  3. After reviewing the detected language details, use the Edit button icon to make the input text editable again. Now you can:
    • Select another sample.
    • Type your own text.
    • Upload a text file.

    For example, enter the following input text and detect the language it’s written in:

     ¡Hola! Me llamo Josefina y vivo en Madrid, España. Soy doctora en un hospital, ¡lo que me mantiene muy ocupada!

  4. Experiment with input of your own.

    Tip: You can use the Bing Translator at https://www.bing.com/translator to generate text in languages you don’t speak!

Identify PII in text

To comply with privacy policies and laws, organizations often need to detect and redact personally identifiable information (PII) such as names, addresses, phone numbers, email addresses, and other personal details.

  1. On the language detection playground page, in the Type drop-down list, select Text PII extraction (or return to the list of AI services and select Azure Language - Text PII extraction).

  2. In the Input text list, select one of the provided sample documents. Then use the Detect button to detect PII values in the text.

    Screenshot of a detected PII in the Playground

  3. After reviewing the detected PII details, use the Edit button to make the input text editable again. Now you can:
    • Select another sample.
    • Type your own text.
    • Upload a text file.

    For example, enter the following input text and detect any PII it contains:

     Maria Garcia called from 020 7946 0958 and asked to send documents to 42 Market Road, London, UK, SW1A 1AA.

  4. Experiment with input of your own. Azure Language can recognize an extensive list of PII. You can see the full list here. A few of those entities include:

    • People names
    • Email addresses
    • Phone numbers
    • Street addresses

Review the sample code

Foundry provides sample code for some Azure Language capabilities. You can use the sample code to begin creating your own client application.

  1. Select the Code tab on the right to view sample code for PII identification, which should be similar to this:

    key = "<your-api-key>"
endpoint = "https://ai-resrce.cognitiveservices.azure.com/"
    
from azure.ai.textanalytics import TextAnalyticsClient
from azure.core.credentials import AzureKeyCredential
    
# Authenticate the client using your key and endpoint 
def authenticate_client():
     ta_credential = AzureKeyCredential(key)
     text_analytics_client = TextAnalyticsClient(
             endpoint=endpoint, 
             credential=ta_credential)
     return text_analytics_client
    
client = authenticate_client()
    
# Example method for detecting sensitive information (PII) from text 
def pii_recognition_example(client):
     documents = [
         "$documents"
     ]
     response = client.recognize_pii_entities(documents, language="en")
     result = [doc for doc in response if not doc.is_error]
     for doc in result:
         print("Redacted Text: {}".format(doc.redacted_text))
         for entity in doc.entities:
             print("Entity: {}".format(entity.text))
             print(" Category: {}".format(entity.category))
             print(" Confidence Score: {}".format(entity.confidence_score))
             print(" Offset: {}".format(entity.offset))
             print(" Length: {}".format(entity.length))
pii_recognition_example(client)

Tip: You can copy the code and run it in your preferred Python development environment - for example Visual Studio Code. You will need to create environment variables for your Azure Language endpoint and key; which you can find in the code sample window.

Summary

In this exercise, you explored how to use a generative AI model and the Azure Language tool in Foundry to analyze text. In many scenarios, the native language capabilities of a generative AI model provide all the natural language processing functionality you need. For more specialized scenarios, the Azure Language tool provides a dedicated service for NLP tasks.

Ask Anton
Anton avatar.
If you have questions about some of the topics covered in this exercise, Ask Anton is a generative AI-based agent that you can ask about AI concepts and Microsoft Foundry. Open the app at https://aka.ms/azk-anton and use the Configure button to enter your Foundry project and model details.

Ask Anton is not a supported Microsoft product or a component of Microsoft Learn or AI Skills Navigator. Just an example of an AI agent for you to explore as you learn about what’s possible with AI.

If you do check out Ask Anton, we’d love you to tell us about your experience!

Clean up

If you have finished exploring Microsoft Foundry, delete any resources that you no longer need. This avoids accruing any unnecessary costs.

  1. Open the Azure portal at https://portal.azure.com and select the resource group that contains the resources you created.
  2. Select Delete resource group and then enter the resource group name to confirm. The resource group is then deleted.