Get started with text analysis in Microsoft Foundry

Azure Language includes Text Analytics, with capabilities such as entity recognition, key phrase extraction, summarization, and sentiment analysis. In this exercise, use Microsoft Foundry, Microsoft’s platform for creating AI applications, to use AI to analyze text. You’ll use Azure Language’s natural language processing functionalities to analyze text. The goal of this exercise is to explore common applications of text analysis techniques.

This exercise takes approximately 20 minutes.

Note: This lab uses the classic Foundry interface. If you are in the new Foundry interface, you need to toggle back to the classic Foundry interface.

Create a project in Microsoft Foundry

In a web browser, open Microsoft Foundry at https://ai.azure.com and sign in using your Azure credentials. Close any tips or quick start panes that are opened the first time you sign in, and if necessary use the Foundry logo at the top left to navigate to the home page, which looks similar to the following image (close the Help pane if it’s open):
Scroll to the bottom of the page, and select the Explore Azure AI Services tile.
On the Azure AI Services page, select the Language + Translator tile.
On the Language + Translator page, select Try the Language playground. Then, when prompted, create a new project with the following settings:
- Project name: Enter a valid name for your project.
- Advanced settings:
  - Subscription: Your Azure subscription
  - Resource group: Create or select a resource group
  - Region: Select any Foundry recommended region
  - AI Foundry or Azure OpenAI Create a new Foundry resource with a valid name
Select Create. Wait for your project to be created. It may take a few minutes.
When the project is created, you will be taken to an Language playground (if not, in the task pane on the left, select Playgrounds and open the Language playground from there.)

The Language playground is a user interface that enables you to try out some Azure Language capabilities.

Prepare for text analysis

Download and extract text.zip at https://aka.ms/ai-text. This archive contains multiple text documents that you’ll use in this exercise.
Return to the Language Playground to try out some of Azure Language’s text analysis capabilities.

Analyze sentiment

Sentiment analysis is a common 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.

In the Language playground, select Classify text. Then select the Analyze sentiment tile.
Upload document-1.txt from the folder where you extracted the downloaded text files.
Select Run. Review the output.

Tip: Notice that the analysis produces an overall sentiment score and individual scores for each sentence.
Select the pencil icon to edit the text. Repeat the analysis for document-2.txt and document-3.txt.

Extract key phrases

Key phrases are the most important pieces of information in text. Let’s use the key phrase extraction capability of Azure Language to pull important information from a review.

In the Language playground, select Extract information. Then select the Extract key phrases tile.
Upload document-1.txt from the folder where you extracted the downloaded text files.
Select Run. Review the output.

Tip: Notice the different phrases extracted in the Details section. These phrases should contribute most to the text’s meaning.
Select the pencil icon to edit the text. Repeat the analysis for document-2.txt and document-3.txt.

Extract named entities

Named entities are words that describe people, places, and objects with proper names. Let’s use the named entity extraction capability of Azure Language to identify types of information in a review.

In the Language playground, select Extract information. Then select the Extract named entities tile.
Upload document-1.txt from the folder where you extracted the downloaded text files.
Select Run. Review the output.

Tip: Notice in the Details section how the extracted entities come with additional information such as type and confidence scores. The confidence score represents the likelihood that the type identified actually belongs to that category.
Select the pencil icon to edit the text. Repeat the analysis for document-2.txt and document-3.txt.

Summarize text

Summarization is a way to distill the main points in a document into a shorter amount of text.

In the Language playground, select Summarize information, then select the Summarize text tile.
Upload document-1.txt from the folder where you extracted the downloaded text files.
Select Run. Review the output.

Tip: Notice the Extractive summary in Details provides rank scores for the most salient sentences.
Select the pencil icon to edit the text. Repeat the analysis for document-2.txt and document-3.txt.

Review the sample code

Select the View code tab to view sample code for text summarization. Below is the same sample code in Python for your reference:


# This example requires environment variables named "AZURE_AI_KEY" and "ENDPOINT_TO_CALL_LANGUAGE_API"
key = os.environ.get('AZURE_AI_KEY')
endpoint = os.environ.get('ENDPOINT_TO_CALL_LANGUAGE_API')

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 summarizing text
def sample_extractive_summarization(client):
    from azure.core.credentials import AzureKeyCredential
    from azure.ai.textanalytics import (
        TextAnalyticsClient,
        ExtractiveSummaryAction
    ) 

    document = [
        "The extractive summarization feature uses natural language processing techniques to locate key sentences in an unstructured text document. "
        "These sentences collectively convey the main idea of the document. This feature is provided as an API for developers. " 
        "They can use it to build intelligent solutions based on the relevant information extracted to support various use cases. "
        "Extractive summarization supports several languages. It is based on pretrained multilingual transformer models, part of our quest for holistic representations. "
        "It draws its strength from transfer learning across monolingual and harness the shared nature of languages to produce models of improved quality and efficiency. "
    ]

    poller = client.begin_analyze_actions(
        document,
        actions=[
            ExtractiveSummaryAction(max_sentence_count=4)
        ],
    )

    document_results = poller.result()
    for result in document_results:
        extract_summary_result = result[0]  # first document, first result
        if extract_summary_result.is_error:
            print("...Is an error with code '{}' and message '{}'".format(
                extract_summary_result.code, extract_summary_result.message
            ))
        else:
            print("Summary extracted: 
{}".format(
                " ".join([sentence.text for sentence in extract_summary_result.sentences]))
            )

sample_extractive_summarization(client)

Tip: If you are using a work or school account to sign into Azure, and you have sufficient permissions in the Azure subscription, you can open the sample code in VS Code for Web to run it, and experiment with editing it.

Clean up

If you don’t intend to do more exercises, delete any resources that you no longer need. This avoids accruing any unnecessary costs.

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

Learn more

To learn more about what you can do with this service, see the Language service page.