Azure Text Analytics client library for Java - Version 5.1.0

Text Analytics is a cloud-based service that provides advanced natural language processing over raw text, and includes six main functions:

  • Sentiment Analysis
  • Language Detection
  • Key Phrase Extraction
  • Named Entity Recognition
  • Personally Identifiable Information (PII) Entity Recognition
  • Linked Entity Recognition
  • Healthcare Entity Recognition
  • Multiple Actions Analysis Per Document

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

Getting started

Prerequisites

Include the Package

<dependency>
    <groupId>com.azure</groupId>
    <artifactId>azure-ai-textanalytics</artifactId>
    <version>5.1.0</version>
</dependency>

Note: This version of the client library defaults to the v3.1 version of the service.

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

SDK version Supported API version of service
5.1.x 3.0, 3.1 (default)
5.0.x 3.0

Create a Cognitive Services or Text Analytics resource

Text Analytics 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 Text Analytics access only, create a Text Analytics resource.

You can create either resource using the

Option 1: Azure Portal

Option 2: Azure CLI

Below is an example of how you can create a Text Analytics resource using the CLI:

# Create a new resource group to hold the text analytics resource -
# if using an existing resource group, skip this step
az group create --name <your-resource-group> --location <location>
# Create text analytics
az cognitiveservices account create \
    --name <your-resource-name> \
    --resource-group <your-resource-group-name> \
    --kind TextAnalytics \
    --sku <sku> \
    --location <location> \
    --yes

For more information about creating the resource or how to get the location and sku information see here

Authenticate the client

In order to interact with the Text Analytics 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 Text Analytics resource in the Azure Portal under the "Keys and Endpoint", or Azure CLI.

# Get the endpoint for the text analytics 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 Text Analytics 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>1.3.1</version>
</dependency>

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 Text Analytics, please refer to the associated documentation.

TokenCredential defaultCredential = new DefaultAzureCredentialBuilder().build();
TextAnalyticsAsyncClient textAnalyticsClient = 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 Text Analytics, 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 Text Analytics 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 Text Analytics 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 a Text Analytics 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 text analytics tasks, including:

Text Analytics Client

Text analytics support both synchronous and asynchronous client creation by using TextAnalyticsClientBuilder,

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

Analyze sentiment

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

List<TextDocumentInput> documents = Arrays.asList(new TextDocumentInput("0",
    "RECORD #333582770390100 | MH | 85986313 | | 054351 | 2/14/2001 12:00:00 AM | "
        + "CORONARY ARTERY DISEASE | Signed | DIS | Admission Date: 5/22/2001 "
        + "Report Status: Signed Discharge Date: 4/24/2001 ADMISSION DIAGNOSIS: "
        + "CORONARY ARTERY DISEASE. HISTORY OF PRESENT ILLNESS: "
        + "The patient is a 54-year-old gentleman with a history of progressive angina over the past"
        + " several months. The patient had a cardiac catheterization in July of this year revealing total"
        + " occlusion of the RCA and 50% left main disease , with a strong family history of coronary"
        + " artery disease with a brother dying at the age of 52 from a myocardial infarction and another"
        + " brother who is status post coronary artery bypass grafting. The patient had a stress"
        + " echocardiogram done on July , 2001 , which showed no wall motion abnormalities,"
        + " but this was a difficult study due to body habitus. The patient went for six minutes with"
        + " minimal ST depressions in the anterior lateral leads , thought due to fatigue and wrist pain,"
        + " his anginal equivalent. Due to the patient's increased symptoms and family history and"
        + " history left main disease with total occasional of his RCA was referred"
        + " for revascularization with open heart surgery."
));
AnalyzeHealthcareEntitiesOptions options = new AnalyzeHealthcareEntitiesOptions().setIncludeStatistics(true);
SyncPoller<AnalyzeHealthcareEntitiesOperationDetail, AnalyzeHealthcareEntitiesPagedIterable>
    syncPoller = textAnalyticsClient.beginAnalyzeHealthcareEntities(documents, options, Context.NONE);
syncPoller.waitForCompletion();
syncPoller.getFinalResult().forEach(
    analyzeHealthcareEntitiesResultCollection -> analyzeHealthcareEntitiesResultCollection.forEach(
        healthcareEntitiesResult -> {
            System.out.println("Document entities: ");
            AtomicInteger ct = new AtomicInteger();
            healthcareEntitiesResult.getEntities().forEach(healthcareEntity -> {
                System.out.printf("\ti = %d, Text: %s, category: %s, subcategory: %s, confidence score: %f.%n",
                    ct.getAndIncrement(), healthcareEntity.getText(), healthcareEntity.getCategory(),
                    healthcareEntity.getSubcategory(), healthcareEntity.getConfidenceScore());
                IterableStream<EntityDataSource> healthcareEntityDataSources =
                    healthcareEntity.getDataSources();
                if (healthcareEntityDataSources != null) {
                    healthcareEntityDataSources.forEach(healthcareEntityLink -> System.out.printf(
                        "\t\tEntity ID in data source: %s, data source: %s.%n",
                        healthcareEntityLink.getEntityId(), healthcareEntityLink.getName()));
                }
            });
            // Healthcare entity relation groups
            healthcareEntitiesResult.getEntityRelations().forEach(entityRelation -> {
                System.out.printf("\tRelation type: %s.%n", entityRelation.getRelationType());
                entityRelation.getRoles().forEach(role -> {
                    final HealthcareEntity entity = role.getEntity();
                    System.out.printf("\t\tEntity text: %s, category: %s, role: %s.%n",
                        entity.getText(), entity.getCategory(), role.getName());
                });
            });
        }));

Analyze multiple actions

The Analyze functionality allows to choose which of the supported Text Analytics features to execute in the same set of documents. Currently, the supported features are: entity recognition, linked entity recognition, Personally Identifiable Information (PII) entity recognition, key phrase extraction, and sentiment analysis.

List<TextDocumentInput> documents = Arrays.asList(
    new TextDocumentInput("0",
        "We went to Contoso Steakhouse located at midtown NYC last week for a dinner party, and we adore"
            + " the spot! They provide marvelous food and they have a great menu. The chief cook happens to be"
            + " the owner (I think his name is John Doe) and he is super nice, coming out of the kitchen and "
            + "greeted us all. We enjoyed very much dining in the place! The Sirloin steak I ordered was tender"
            + " and juicy, and the place was impeccably clean. You can even pre-order from their online menu at"
            + " www.contososteakhouse.com, call 312-555-0176 or send email to order@contososteakhouse.com! The"
            + " only complaint I have is the food didn't come fast enough. Overall I highly recommend it!")
);

SyncPoller<AnalyzeActionsOperationDetail, AnalyzeActionsResultPagedIterable> syncPoller =
    textAnalyticsClient.beginAnalyzeActions(documents,
        new TextAnalyticsActions().setDisplayName("{tasks_display_name}")
            .setExtractKeyPhrasesActions(new ExtractKeyPhrasesAction())
            .setRecognizePiiEntitiesActions(new RecognizePiiEntitiesAction()),
        new AnalyzeActionsOptions().setIncludeStatistics(false),
        Context.NONE);
syncPoller.waitForCompletion();
syncPoller.getFinalResult().forEach(analyzeActionsResult -> {
    System.out.println("Key phrases extraction action results:");
    analyzeActionsResult.getExtractKeyPhrasesResults().forEach(actionResult -> {
        AtomicInteger counter = new AtomicInteger();
        if (!actionResult.isError()) {
            for (ExtractKeyPhraseResult extractKeyPhraseResult : actionResult.getDocumentsResults()) {
                System.out.printf("%n%s%n", documents.get(counter.getAndIncrement()));
                System.out.println("Extracted phrases:");
                extractKeyPhraseResult.getKeyPhrases()
                    .forEach(keyPhrases -> System.out.printf("\t%s.%n", keyPhrases));
            }
        }
    });
    System.out.println("PII entities recognition action results:");
    analyzeActionsResult.getRecognizePiiEntitiesResults().forEach(actionResult -> {
        AtomicInteger counter = new AtomicInteger();
        if (!actionResult.isError()) {
            for (RecognizePiiEntitiesResult entitiesResult : actionResult.getDocumentsResults()) {
                System.out.printf("%n%s%n", documents.get(counter.getAndIncrement()));
                PiiEntityCollection piiEntityCollection = entitiesResult.getEntities();
                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, offset: %s, confidence score: %f.%n",
                    entity.getText(), entity.getCategory(), entity.getSubcategory(), entity.getOffset(),
                    entity.getConfidenceScore()));
            }
        }
    });
});

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 EspaƱol.", "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.

Impressions