Azure Text Analytics client library for Java - version 5.5.3

The Azure Cognitive Service for Language is a cloud-based service that provides Natural Language Processing (NLP) features for understanding and analyzing text, and includes the following main features:

• Sentiment Analysis
• Entity Recognition (Named, Linked, and Personally Identifiable Information (PII) entities)
• Language Detection
• Key Phrase Extraction
• Multiple Actions Analysis Per Document
• Healthcare Entities Analysis
• Abstractive Text Summarization
• Extractive Text Summarization
• Custom Named Entity Recognition
• Custom Text Classification

Source code | Package (Maven) | API reference documentation | Product Documentation | Samples

Getting started

Prerequisites

• A Java Development Kit (JDK), version 8 or later.
  • Here are details about Java 8 client compatibility with Azure Certificate Authority.
• Azure Subscription
• Cognitive Services or Language service account to use this package.

Include the Package

Include the BOM file

Please include the azure-sdk-bom to your project to take dependency on GA version of the library. In the following snippet, replace the {bom_version_to_target} placeholder with the version number. To learn more about the BOM, see the AZURE SDK BOM README.

<dependencyManagement>
    <dependencies>
        <dependency>
            <groupId>com.azure</groupId>
            <artifactId>azure-sdk-bom</artifactId>
            <version>{bom_version_to_target}</version>
            <type>pom</type>
            <scope>import</scope>
        </dependency>
    </dependencies>
</dependencyManagement>

and then include the direct dependency in the dependencies section without the version tag.

<dependencies>
  <dependency>
    <groupId>com.azure</groupId>
    <artifactId>azure-ai-textanalytics</artifactId>
  </dependency>
</dependencies>

Include direct dependency

If you want to take dependency on a particular version of the library that is not present in the BOM, add the direct dependency to your project as follows.

<dependency>
    <groupId>com.azure</groupId>
    <artifactId>azure-ai-textanalytics</artifactId>
    <version>5.5.3</version>
</dependency>

Note: This version of the client library defaults to the 2023-04-01 version of the service. It is a newer version than 3_0, 3_1 and 2022-05-01.

This table shows the relationship between SDK services and supported API versions of the service:

Create a Cognitive Services or Language Service resource

The Language service supports both multi-service and single-service access. Create a Cognitive Services resource if you plan to access multiple cognitive services under a single endpoint/key. For Language service access only, create a Language service resource.

You can create the resource using the Azure Portal or Azure CLI following the steps in this document.

Authenticate the client

In order to interact with the Language service, you will need to create an instance of the Text Analytics client, both the asynchronous and synchronous clients can be created by using TextAnalyticsClientBuilder invoking buildClient() creates a synchronous client while buildAsyncClient() creates its asynchronous counterpart.

You will need an endpoint and either a key or AAD TokenCredential to instantiate a client object.

Looking up the endpoint

You can find the endpoint for your Language service resource in the Azure Portal under the "Keys and Endpoint", or Azure CLI.

# Get the endpoint for the Language service resource
az cognitiveservices account show --name "resource-name" --resource-group "resource-group-name" --query "endpoint"

Create a Text Analytics client with key credential

Once you have the value for the key, provide it as a string to the AzureKeyCredential. This can be found in the Azure Portal under the "Keys and Endpoint" section in your created Language service resource or by running the following Azure CLI command:

az cognitiveservices account keys list --resource-group <your-resource-group-name> --name <your-resource-name>

Use the key as the credential parameter to authenticate the client:

TextAnalyticsClient textAnalyticsClient = new TextAnalyticsClientBuilder()
    .credential(new AzureKeyCredential("{key}"))
    .endpoint("{endpoint}")
    .buildClient();

The Azure Text Analytics client library provides a way to rotate the existing key.

AzureKeyCredential credential = new AzureKeyCredential("{key}");
TextAnalyticsClient textAnalyticsClient = new TextAnalyticsClientBuilder()
    .credential(credential)
    .endpoint("{endpoint}")
    .buildClient();

credential.update("{new_key}");

Create a Text Analytics client with Azure Active Directory credential

Azure SDK for Java supports an Azure Identity package, making it easy to get credentials from Microsoft identity platform.

Authentication with AAD requires some initial setup:

• Add the Azure Identity package

<dependency>
    <groupId>com.azure</groupId>
    <artifactId>azure-identity</artifactId>
    <version>1.13.1</version>
</dependency>

• Register a new Azure Active Directory application
• Grant access to Language service by assigning the "Cognitive Services User" role to your service principal.

After setup, you can choose which type of credential from azure.identity to use. As an example, DefaultAzureCredential can be used to authenticate the client: Set the values of the client ID, tenant ID, and client secret of the AAD application as environment variables: AZURE_CLIENT_ID, AZURE_TENANT_ID, AZURE_CLIENT_SECRET.

Authorization is easiest using DefaultAzureCredential. It finds the best credential to use in its running environment. For more information about using Azure Active Directory authorization with Language service, please refer to the associated documentation.

TokenCredential defaultCredential = new DefaultAzureCredentialBuilder().build();
TextAnalyticsAsyncClient textAnalyticsAsyncClient = new TextAnalyticsClientBuilder()
    .endpoint("{endpoint}")
    .credential(defaultCredential)
    .buildAsyncClient();

Key concepts

Text Analytics client

The Text Analytics client library provides a TextAnalyticsClient and TextAnalyticsAsyncClient to do analysis on batches of documents. It provides both synchronous and asynchronous operations to access a specific use of Language service, such as language detection or key phrase extraction.

Input

A text input, also called a document, is a single unit of document to be analyzed by the predictive models in the Language service. Operations on a Text Analytics client may take a single document or a collection of documents to be analyzed as a batch. See service limitations for the document, including document length limits, maximum batch size, and supported text encoding.

Operation on multiple documents

For each supported operation, the Text Analytics client provides method overloads to take a single document, a batch of documents as strings, or a batch of either TextDocumentInput or DetectLanguageInput objects. The overload taking the TextDocumentInput or DetectLanguageInput batch allows callers to give each document a unique ID, indicate that the documents in the batch are written in different languages, or provide a country hint about the language of the document.

Return value

An operation result, such as AnalyzeSentimentResult, is the result of a Language service operation, containing a prediction or predictions about a single document and a list of warnings inside of it. An operation's result type also may optionally include information about the input document and how it was processed. An operation result contains a isError property that allows to identify if an operation executed was successful or unsuccessful for the given document. When the operation results an error, you can simply call getError() to get TextAnalyticsError which contains the reason why it is unsuccessful. If you are interested in how many characters are in your document, or the number of operation transactions that have gone through, simply call getStatistics() to get the TextDocumentStatistics which contains both information.

Return value collection

An operation result collection, such as AnalyzeSentimentResultCollection, which is the collection of the result of analyzing sentiment operation. It also includes the model version of the operation and statistics of the batch documents.

Note: It is recommended to use the batch methods when working on production environments as they allow you to send one request with multiple documents. This is more performant than sending a request per each document.

Examples

The following sections provide several code snippets covering some of the most common Language service tasks, including:

• Analyze Sentiment
• Detect Language
• Extract Key Phrases
• Recognize Named Entities
• Recognize Personally Identifiable Information Entities
• Recognize Linked Entities
• Analyze Healthcare Entities
• Analyze Multiple Actions
• Custom Entities Recognition
• Custom Text Classification
• Abstractive Text Summarization
• Extractive Text Summarization

Text Analytics Client

Language service supports both synchronous and asynchronous client creation by using TextAnalyticsClientBuilder,

TextAnalyticsClient textAnalyticsClient = new TextAnalyticsClientBuilder()
    .credential(new AzureKeyCredential("{key}"))
    .endpoint("{endpoint}")
    .buildClient();

or

TextAnalyticsAsyncClient textAnalyticsAsyncClient = new TextAnalyticsClientBuilder()
    .credential(new AzureKeyCredential("{key}"))
    .endpoint("{endpoint}")
    .buildAsyncClient();

Analyze sentiment

Run a predictive model to identify the positive, negative, neutral or mixed sentiment contained in the provided document or batch of documents.

String document = "The hotel was dark and unclean. I like microsoft.";
DocumentSentiment documentSentiment = textAnalyticsClient.analyzeSentiment(document);
System.out.printf("Analyzed document sentiment: %s.%n", documentSentiment.getSentiment());
documentSentiment.getSentences().forEach(sentenceSentiment ->
    System.out.printf("Analyzed sentence sentiment: %s.%n", sentenceSentiment.getSentiment()));

For samples on using the production recommended option AnalyzeSentimentBatch see here.

To get more granular information about the opinions related to aspects of a product/service, also knows as Aspect-based Sentiment Analysis in Natural Language Processing (NLP), see sample on sentiment analysis with opinion mining see here.

Please refer to the service documentation for a conceptual discussion of sentiment analysis.

Detect language

Run a predictive model to determine the language that the provided document or batch of documents are written in.

String document = "Bonjour tout le monde";
DetectedLanguage detectedLanguage = textAnalyticsClient.detectLanguage(document);
System.out.printf("Detected language name: %s, ISO 6391 name: %s, confidence score: %f.%n",
    detectedLanguage.getName(), detectedLanguage.getIso6391Name(), detectedLanguage.getConfidenceScore());

For samples on using the production recommended option DetectLanguageBatch see here. Please refer to the service documentation for a conceptual discussion of language detection.

Extract key phrases

Run a model to identify a collection of significant phrases found in the provided document or batch of documents.

String document = "My cat might need to see a veterinarian.";
System.out.println("Extracted phrases:");
textAnalyticsClient.extractKeyPhrases(document).forEach(keyPhrase -> System.out.printf("%s.%n", keyPhrase));

For samples on using the production recommended option ExtractKeyPhrasesBatch see here. Please refer to the service documentation for a conceptual discussion of key phrase extraction.

Recognize named entities

Run a predictive model to identify a collection of named entities in the provided document or batch of documents and categorize those entities into categories such as person, location, or organization. For more information on available categories, see Named Entity Categories.

String document = "Satya Nadella is the CEO of Microsoft";
textAnalyticsClient.recognizeEntities(document).forEach(entity ->
    System.out.printf("Recognized entity: %s, category: %s, subcategory: %s, confidence score: %f.%n",
        entity.getText(), entity.getCategory(), entity.getSubcategory(), entity.getConfidenceScore()));

For samples on using the production recommended option RecognizeEntitiesBatch see here. Please refer to the service documentation for a conceptual discussion of named entity recognition.

Recognize Personally Identifiable Information entities

Run a predictive model to identify a collection of Personally Identifiable Information(PII) entities in the provided document. It recognizes and categorizes PII entities in its input text, such as Social Security Numbers, bank account information, credit card numbers, and more. This endpoint is only supported for API versions v3.1-preview.1 and above.

String document = "My SSN is 859-98-0987";
PiiEntityCollection piiEntityCollection = textAnalyticsClient.recognizePiiEntities(document);
System.out.printf("Redacted Text: %s%n", piiEntityCollection.getRedactedText());
piiEntityCollection.forEach(entity -> System.out.printf(
    "Recognized Personally Identifiable Information entity: %s, entity category: %s, entity subcategory: %s,"
        + " confidence score: %f.%n",
    entity.getText(), entity.getCategory(), entity.getSubcategory(), entity.getConfidenceScore()));

For samples on using the production recommended option RecognizePiiEntitiesBatch see here. Please refer to the service documentation for supported PII entity types.

Recognize linked entities

Run a predictive model to identify a collection of entities found in the provided document or batch of documents, and include information linking the entities to their corresponding entries in a well-known knowledge base.

String document = "Old Faithful is a geyser at Yellowstone Park.";
textAnalyticsClient.recognizeLinkedEntities(document).forEach(linkedEntity -> {
    System.out.println("Linked Entities:");
    System.out.printf("Name: %s, entity ID in data source: %s, URL: %s, data source: %s.%n",
        linkedEntity.getName(), linkedEntity.getDataSourceEntityId(), linkedEntity.getUrl(), linkedEntity.getDataSource());
    linkedEntity.getMatches().forEach(match ->
        System.out.printf("Text: %s, confidence score: %f.%n", match.getText(), match.getConfidenceScore()));
});

For samples on using the production recommended option RecognizeLinkedEntitiesBatch see here. Please refer to the service documentation for a conceptual discussion of entity linking.

Analyze healthcare entities

Text Analytics for health is a containerized service that extracts and labels relevant medical information from unstructured texts such as doctor's notes, discharge summaries, clinical documents, and electronic health records.

• Healthcare entities recognition

For more information see How to: Use Text Analytics for health.

Custom entities recognition

Custom NER is one of the custom features offered by Azure Cognitive Service for Language. It is a cloud-based API service that applies machine-learning intelligence to enable you to build custom models for custom named entity recognition tasks.

• Custom entities recognition

For more information see How to use: Custom Entities Recognition.

Custom text classification

Custom text classification is one of the custom features offered by Azure Cognitive Service for Language. It is a cloud-based API service that applies machine-learning intelligence to enable you to build custom models for text classification tasks.

• Single label classification

• Multi label classification

For more information see How to use: Custom Text Classification.

Analyze multiple actions

The Analyze functionality allows choosing which of the supported Language service features to execute in the same set of documents. Currently, the supported features are:

• Named Entities Recognition
• PII Entities Recognition
• Linked Entity Recognition
• Key Phrase Extraction
• Sentiment Analysis
• Healthcare Analysis
• Custom Entity Recognition (API version 2022-05-01 and newer)
• Custom Single-Label Classification (API version 2022-05-01 and newer)
• Custom Multi-Label Classification (API version 2022-05-01 and newer)
• Abstractive Text Summarization (API version 2023-04-01 and newer)
• Extractive Text Summarization (API version 2023-04-01 and newer)

Sample: Multiple action analysis

For more examples, such as asynchronous samples, refer to here.

Troubleshooting

General

Text Analytics clients raise exceptions. For example, if you try to detect the languages of a batch of text with same document IDs, 400 error is return that indicating bad request. In the following code snippet, the error is handled gracefully by catching the exception and display the additional information about the error.

List<DetectLanguageInput> documents = Arrays.asList(
    new DetectLanguageInput("1", "This is written in English.", "us"),
    new DetectLanguageInput("1", "Este es un documento  escrito en Español.", "es")
);

try {
    textAnalyticsClient.detectLanguageBatchWithResponse(documents, null, Context.NONE);
} catch (HttpResponseException e) {
    System.out.println(e.getMessage());
}

Enable client logging

You can set the AZURE_LOG_LEVEL environment variable to view logging statements made in the client library. For example, setting AZURE_LOG_LEVEL=2 would show all informational, warning, and error log messages. The log levels can be found here: log levels.

Default HTTP Client

All client libraries by default use the Netty HTTP client. Adding the above dependency will automatically configure the client library to use the Netty HTTP client. Configuring or changing the HTTP client is detailed in the HTTP clients wiki.

Default SSL library

All client libraries, by default, use the Tomcat-native Boring SSL library to enable native-level performance for SSL operations. The Boring SSL library is an uber jar containing native libraries for Linux / macOS / Windows, and provides better performance compared to the default SSL implementation within the JDK. For more information, including how to reduce the dependency size, refer to the performance tuning section of the wiki.

Next steps

• Samples are explained in detail here.

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution.

When you submit a pull request, a CLA-bot will automatically determine whether you need to provide a CLA and decorate the PR appropriately (e.g., label, comment). Simply follow the instructions provided by the bot. You will only need to do this once across all repos using our CLA.

This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact opencode@microsoft.com with any additional questions or comments.