Package com.azure.ai.textanalytics

Class TextAnalyticsAsyncClient

java.lang.Object
com.azure.ai.textanalytics.TextAnalyticsAsyncClient
public final class TextAnalyticsAsyncClient extends Object
This class provides an asynchronous client that contains all the operations that apply to Azure Text Analytics. Operations allowed by the client are language detection, entities recognition, linked entities recognition, key phrases extraction, and sentiment analysis of a document or a list of documents.

Instantiating an asynchronous Text Analytics Client

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

View TextAnalyticsClientBuilder for additional ways to construct the client.

See Also:

  • Method Details

    • getDefaultCountryHint

      public String getDefaultCountryHint()
      Gets default country hint code.
      Returns:
      The default country hint code

    • getDefaultLanguage

      public String getDefaultLanguage()
      Gets default language when the builder is setup.
      Returns:
      The default language

    • detectLanguage

      public Mono<DetectedLanguage> detectLanguage(String document)
      Returns the detected language and a confidence score between zero and one. Scores close to one indicate 100% certainty that the identified language is true. This method will use the default country hint that sets up in TextAnalyticsClientBuilder.defaultCountryHint(String). If none is specified, service will use 'US' as the country hint.

      Code sample

      Detects language in a document. Subscribes to the call asynchronously and prints out the detected language details when a response is received.

       String document = "Bonjour tout le monde";
 textAnalyticsAsyncClient.detectLanguage(document).subscribe(detectedLanguage ->
     System.out.printf("Detected language name: %s, ISO 6391 Name: %s, confidence score: %f.%n",
         detectedLanguage.getName(), detectedLanguage.getIso6391Name(), detectedLanguage.getConfidenceScore()));
      Parameters:
      document - The document to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      Returns:
      A Mono containing the detected language of the document.
      Throws:
      NullPointerException - if the document is null.
      TextAnalyticsException - if the response returned with an error.

    • detectLanguage

      public Mono<DetectedLanguage> detectLanguage(String document, String countryHint)
      Returns a Response contains the detected language and a confidence score between zero and one. Scores close to one indicate 100% certainty that the identified language is true.

      Code sample

      Detects language with http response in a document with a provided country hint. Subscribes to the call asynchronously and prints out the detected language details when a response is received.

       String document = "This text is in English";
 String countryHint = "US";
 textAnalyticsAsyncClient.detectLanguage(document, countryHint).subscribe(detectedLanguage ->
     System.out.printf("Detected language name: %s, ISO 6391 Name: %s, confidence score: %f.%n",
         detectedLanguage.getName(), detectedLanguage.getIso6391Name(), detectedLanguage.getConfidenceScore()));
      Parameters:
      document - The document to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      countryHint - Accepts 2-letter country codes specified by ISO 3166-1 alpha-2. Defaults to "US" if not specified. To remove this behavior you can reset this parameter by setting this value to empty string countryHint = "" or "none".
      Returns:
      A Mono contains a detected language of the document.
      Throws:
      NullPointerException - if the document is null.
      TextAnalyticsException - if the response returned with an error.

    • detectLanguageBatch

      public Mono<DetectLanguageResultCollection> detectLanguageBatch(Iterable<String> documents, String countryHint, TextAnalyticsRequestOptions options)
      Returns the detected language for each of documents with the provided country hint and request option.

      Code sample

      Detects language in a list of documents with a provided country hint and request option for the batch. Subscribes to the call asynchronously and prints out the detected language details when a response is received. 

       List<String> documents = Arrays.asList(
     "This is written in English",
     "Este es un documento  escrito en Español."
 );
 textAnalyticsAsyncClient.detectLanguageBatch(documents, "US", null).subscribe(
     batchResult -> {
         // Batch statistics
         TextDocumentBatchStatistics batchStatistics = batchResult.getStatistics();
         System.out.printf("Batch statistics, transaction count: %s, valid document count: %s.%n",
             batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());
         // Batch result of languages
         for (DetectLanguageResult detectLanguageResult : batchResult) {
             DetectedLanguage detectedLanguage = detectLanguageResult.getPrimaryLanguage();
             System.out.printf("Detected language name: %s, ISO 6391 Name: %s, confidence score: %f.%n",
                 detectedLanguage.getName(), detectedLanguage.getIso6391Name(),
                 detectedLanguage.getConfidenceScore());
         }
     });
      Parameters:
      documents - The list of documents to detect languages for. For text length limits, maximum batch size, and supported text encoding, see data limits.
      countryHint - Accepts two letter country codes specified by ISO 3166-1 alpha-2. Defaults to "US" if not specified. To remove this behavior you can reset this parameter by setting this value to empty string countryHint = "" or "none".
      options - The options to configure the scoring model for documents and show statistics.
      Returns:
      A Mono contains a DetectLanguageResultCollection.
      Throws:
      NullPointerException - if documents is null.

    • detectLanguageBatchWithResponse

      public Mono<Response<DetectLanguageResultCollection>> detectLanguageBatchWithResponse(Iterable<DetectLanguageInput> documents, TextAnalyticsRequestOptions options)
      Returns the detected language for a batch of document with provided request options.

      Code sample

      Detects language in a batch of document with provided request options. Subscribes to the call asynchronously and prints out the detected language details when a response is received.

       List<DetectLanguageInput> detectLanguageInputs1 = Arrays.asList(
     new DetectLanguageInput("1", "This is written in English.", "US"),
     new DetectLanguageInput("2", "Este es un documento  escrito en Español.", "ES")
 );

 TextAnalyticsRequestOptions requestOptions = new TextAnalyticsRequestOptions().setIncludeStatistics(true);

 textAnalyticsAsyncClient.detectLanguageBatchWithResponse(detectLanguageInputs1, requestOptions)
     .subscribe(response -> {
         // Response's status code
         System.out.printf("Status code of request response: %d%n", response.getStatusCode());

         DetectLanguageResultCollection resultCollection = response.getValue();
         // Batch statistics
         TextDocumentBatchStatistics batchStatistics = resultCollection.getStatistics();
         System.out.printf("Batch statistics, transaction count: %s, valid document count: %s.%n",
             batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());
         // Batch result of languages
         for (DetectLanguageResult detectLanguageResult : resultCollection) {
             DetectedLanguage detectedLanguage = detectLanguageResult.getPrimaryLanguage();
             System.out.printf("Detected language name: %s, ISO 6391 Name: %s, confidence score: %f.%n",
                 detectedLanguage.getName(), detectedLanguage.getIso6391Name(),
                 detectedLanguage.getConfidenceScore());
         }
     });
      Parameters:
      documents - The list of documents to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      options - The options to configure the scoring model for documents and show statistics.
      Returns:
      A Mono contains a Response which contains a DetectLanguageResultCollection.
      Throws:
      NullPointerException - if documents is null.

    • recognizeEntities

      public Mono<CategorizedEntityCollection> recognizeEntities(String document)
      Returns a list of general categorized entities in the provided document. For a list of supported entity types, check: this. For a list of enabled languages, check: this. This method will use the default language that can be set by using method TextAnalyticsClientBuilder.defaultLanguage(String). If none is specified, service will use 'en' as the language.

      Code sample

      Recognize entities in a document. Subscribes to the call asynchronously and prints out the recognized entity details when a response is received.

       String document = "Satya Nadella is the CEO of Microsoft";
 textAnalyticsAsyncClient.recognizeEntities(document)
     .subscribe(entityCollection -> entityCollection.forEach(entity ->
         System.out.printf("Recognized categorized entity: %s, category: %s, confidence score: %f.%n",
         entity.getText(),
         entity.getCategory(),
         entity.getConfidenceScore())));
      Parameters:
      document - The document to recognize entities for. For text length limits, maximum batch size, and supported text encoding, see data limits.
      Returns:
      A Mono contains a recognized categorized entities collection.
      Throws:
      NullPointerException - if document is null.
      TextAnalyticsException - if the response returned with an error.

    • recognizeEntities

      public Mono<CategorizedEntityCollection> recognizeEntities(String document, String language)
      Returns a list of general categorized entities in the provided document. For a list of supported entity types, check: this. For a list of enabled languages, check: this.

      Code sample

      Recognize entities in a document with provided language code. Subscribes to the call asynchronously and prints out the entity details when a response is received.

       String document = "Satya Nadella is the CEO of Microsoft";
 textAnalyticsAsyncClient.recognizeEntities(document, "en")
     .subscribe(entityCollection -> entityCollection.forEach(entity ->
         System.out.printf("Recognized categorized entity: %s, category: %s, confidence score: %f.%n",
         entity.getText(),
         entity.getCategory(),
         entity.getConfidenceScore())));
      Parameters:
      document - the text to recognize entities for. For text length limits, maximum batch size, and supported text encoding, see data limits.
      language - The 2 letter ISO 639-1 representation of language. If not set, uses "en" for English as default.
      Returns:
      A Mono contains a recognized categorized entities collection.
      Throws:
      NullPointerException - if document is null.
      TextAnalyticsException - if the response returned with an error.

    • recognizeEntitiesBatch

      public Mono<RecognizeEntitiesResultCollection> recognizeEntitiesBatch(Iterable<String> documents, String language, TextAnalyticsRequestOptions options)
      Returns a list of general categorized entities for the provided list of documents with the provided language code and request options.

      Code sample

      Recognize entities in a document with the provided language code. Subscribes to the call asynchronously and prints out the entity details when a response is received.

       List<String> documents = Arrays.asList(
     "I had a wonderful trip to Seattle last week.", "I work at Microsoft.");

 textAnalyticsAsyncClient.recognizeEntitiesBatch(documents, "en", null)
     .subscribe(batchResult -> {
         // Batch statistics
         TextDocumentBatchStatistics batchStatistics = batchResult.getStatistics();
         System.out.printf("Batch statistics, transaction count: %s, valid document count: %s.%n",
             batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());
         // Batch Result of entities
         batchResult.forEach(recognizeEntitiesResult ->
             recognizeEntitiesResult.getEntities().forEach(entity -> System.out.printf(
                 "Recognized categorized entity: %s, category: %s, confidence score: %f.%n",
                     entity.getText(), entity.getCategory(), entity.getConfidenceScore())));
     });
      Parameters:
      documents - A list of documents to recognize entities for. For text length limits, maximum batch size, and supported text encoding, see data limits.
      language - The 2 letter ISO 639-1 representation of language. If not set, uses "en" for English as default.
      options - The options to configure the scoring model for documents and show statistics.
      Returns:
      A Mono contains a RecognizeEntitiesResultCollection.
      Throws:
      NullPointerException - if documents is null.

    • recognizeEntitiesBatchWithResponse

      public Mono<Response<RecognizeEntitiesResultCollection>> recognizeEntitiesBatchWithResponse(Iterable<TextDocumentInput> documents, TextAnalyticsRequestOptions options)
      Returns a list of general categorized entities for the provided list of document with provided request options.

      Code sample

      Recognize entities in a list of document. Subscribes to the call asynchronously and prints out the entity details when a response is received.

       List<TextDocumentInput> textDocumentInputs1 = Arrays.asList(
     new TextDocumentInput("0", "I had a wonderful trip to Seattle last week.").setLanguage("en"),
     new TextDocumentInput("1", "I work at Microsoft.").setLanguage("en"));

 TextAnalyticsRequestOptions requestOptions = new TextAnalyticsRequestOptions().setIncludeStatistics(true);

 textAnalyticsAsyncClient.recognizeEntitiesBatchWithResponse(textDocumentInputs1, requestOptions)
     .subscribe(response -> {
         // Response's status code
         System.out.printf("Status code of request response: %d%n", response.getStatusCode());
         RecognizeEntitiesResultCollection resultCollection = response.getValue();

         // Batch statistics
         TextDocumentBatchStatistics batchStatistics = resultCollection.getStatistics();
         System.out.printf("Batch statistics, transaction count: %s, valid document count: %s.%n",
             batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());

         resultCollection.forEach(recognizeEntitiesResult ->
             recognizeEntitiesResult.getEntities().forEach(entity -> System.out.printf(
                 "Recognized categorized entity: %s, category: %s, confidence score: %f.%n",
                 entity.getText(),
                 entity.getCategory(),
                 entity.getConfidenceScore())));
     });
      Parameters:
      documents - A list of documents to recognize entities for. For text length limits, maximum batch size, and supported text encoding, see data limits.
      options - The options to configure the scoring model for documents and show statistics.
      Returns:
      A Mono contains a Response which contains a RecognizeEntitiesResultCollection.
      Throws:
      NullPointerException - if documents is null.

    • recognizePiiEntities

      public Mono<PiiEntityCollection> recognizePiiEntities(String document)
      Returns a list of Personally Identifiable Information(PII) entities in the provided document. For a list of supported entity types, check: this. For a list of enabled languages, check: this. This method will use the default language that is set using TextAnalyticsClientBuilder.defaultLanguage(String). If none is specified, service will use 'en' as the language.

      Code sample

      Recognize the PII entities details in a document. Subscribes to the call asynchronously and prints out the recognized entity details when a response is received.

       String document = "My SSN is 859-98-0987";
 textAnalyticsAsyncClient.recognizePiiEntities(document).subscribe(piiEntityCollection -> {
     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()));
 });
      Parameters:
      document - The document to recognize PII entities details for. For text length limits, maximum batch size, and supported text encoding, see data limits.
      Returns:
      A Mono contains a recognized PII entities collection.
      Throws:
      NullPointerException - if document is null.
      TextAnalyticsException - if the response returned with an error.

    • recognizePiiEntities

      public Mono<PiiEntityCollection> recognizePiiEntities(String document, String language)
      Returns a list of Personally Identifiable Information(PII) entities in the provided document with provided language code. For a list of supported entity types, check: this. For a list of enabled languages, check: this.

      Code sample

      Recognize the PII entities details in a document with provided language code. Subscribes to the call asynchronously and prints out the entity details when a response is received.

       String document = "My SSN is 859-98-0987";
 textAnalyticsAsyncClient.recognizePiiEntities(document, "en")
     .subscribe(piiEntityCollection -> {
         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()));
     });
      Parameters:
      document - the text to recognize PII entities details for. For text length limits, maximum batch size, and supported text encoding, see data limits.
      language - The 2 letter ISO 639-1 representation of language. If not set, uses "en" for English as default.
      Returns:
      A Mono contains a recognized PII entities collection.
      Throws:
      NullPointerException - if document is null.
      TextAnalyticsException - if the response returned with an error.

    • recognizePiiEntities

      public Mono<PiiEntityCollection> recognizePiiEntities(String document, String language, RecognizePiiEntitiesOptions options)
      Returns a list of Personally Identifiable Information(PII) entities in the provided document with provided language code. For a list of supported entity types, check: this. For a list of enabled languages, check: this.

      Code sample

      Recognize the PII entities details in a document with provided language code and RecognizePiiEntitiesOptions. Subscribes to the call asynchronously and prints out the entity details when a response is received.

       String document = "My SSN is 859-98-0987";
 textAnalyticsAsyncClient.recognizePiiEntities(document, "en",
     new RecognizePiiEntitiesOptions().setDomainFilter(PiiEntityDomain.PROTECTED_HEALTH_INFORMATION))
     .subscribe(piiEntityCollection -> {
         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()));
     });
      Parameters:
      document - the text to recognize PII entities details for. For text length limits, maximum batch size, and supported text encoding, see data limits.
      language - The 2-letter ISO 639-1 representation of language. If not set, uses "en" for English as default.
      options - The additional configurable options that may be passed when recognizing PII entities.
      Returns:
      A Mono contains a recognized PII entities collection.
      Throws:
      NullPointerException - if document is null.
      TextAnalyticsException - if the response returned with an error.

    • recognizePiiEntitiesBatch

      public Mono<RecognizePiiEntitiesResultCollection> recognizePiiEntitiesBatch(Iterable<String> documents, String language, RecognizePiiEntitiesOptions options)
      Returns a list of Personally Identifiable Information(PII) entities for the provided list of documents with the provided language code and request options.

      Code sample

      Recognize Personally Identifiable Information entities in a document with the provided language code. Subscribes to the call asynchronously and prints out the entity details when a response is received.

       List<String> documents = Arrays.asList(
     "My SSN is 859-98-0987.",
     "Visa card 0111 1111 1111 1111."
 );

 // Show statistics and model version
 RecognizePiiEntitiesOptions requestOptions = new RecognizePiiEntitiesOptions().setIncludeStatistics(true)
     .setModelVersion("latest");

 textAnalyticsAsyncClient.recognizePiiEntitiesBatch(documents, "en", requestOptions)
     .subscribe(piiEntitiesResults -> {
         // Batch statistics
         TextDocumentBatchStatistics batchStatistics = piiEntitiesResults.getStatistics();
         System.out.printf("Batch statistics, transaction count: %s, valid document count: %s.%n",
             batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());

         piiEntitiesResults.forEach(recognizePiiEntitiesResult -> {
             PiiEntityCollection piiEntityCollection = recognizePiiEntitiesResult.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, confidence score: %f.%n",
                 entity.getText(), entity.getCategory(), entity.getSubcategory(), entity.getConfidenceScore()));
         });
     });
      Parameters:
      documents - A list of documents to recognize PII entities for. For text length limits, maximum batch size, and supported text encoding, see data limits.
      language - The 2 letter ISO 639-1 representation of language. If not set, uses "en" for English as default.
      options - The additional configurable options that may be passed when recognizing PII entities.
      Returns:
      A Mono contains a RecognizePiiEntitiesResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.

    • recognizePiiEntitiesBatchWithResponse

      public Mono<Response<RecognizePiiEntitiesResultCollection>> recognizePiiEntitiesBatchWithResponse(Iterable<TextDocumentInput> documents, RecognizePiiEntitiesOptions options)
      Returns a list of Personally Identifiable Information entities for the provided list of document with provided request options.

      Code sample

      Recognize the PII entities details with http response in a list of document with provided request options. Subscribes to the call asynchronously and prints out the entity details when a response is received.

       List<TextDocumentInput> textDocumentInputs1 = Arrays.asList(
     new TextDocumentInput("0", "My SSN is 859-98-0987."),
     new TextDocumentInput("1", "Visa card 0111 1111 1111 1111."));

 // Show statistics and model version
 RecognizePiiEntitiesOptions requestOptions = new RecognizePiiEntitiesOptions().setIncludeStatistics(true)
     .setModelVersion("latest");

 textAnalyticsAsyncClient.recognizePiiEntitiesBatchWithResponse(textDocumentInputs1, requestOptions)
     .subscribe(response -> {
         RecognizePiiEntitiesResultCollection piiEntitiesResults = response.getValue();
         // Batch statistics
         TextDocumentBatchStatistics batchStatistics = piiEntitiesResults.getStatistics();
         System.out.printf("Batch statistics, transaction count: %s, valid document count: %s.%n",
             batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());

         piiEntitiesResults.forEach(recognizePiiEntitiesResult -> {
             PiiEntityCollection piiEntityCollection = recognizePiiEntitiesResult.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, confidence score: %f.%n",
                 entity.getText(), entity.getCategory(), entity.getSubcategory(), entity.getConfidenceScore()));
         });
     });
      Parameters:
      documents - A list of documents to recognize PII entities for. For text length limits, maximum batch size, and supported text encoding, see data limits.
      options - The additional configurable options that may be passed when recognizing PII entities.
      Returns:
      A Mono contains a Response which contains a RecognizePiiEntitiesResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.

    • recognizeLinkedEntities

      public Mono<LinkedEntityCollection> recognizeLinkedEntities(String document)
      Returns a list of recognized entities with links to a well-known knowledge base for the provided document. See this for supported languages in Text Analytics API. This method will use the default language that can be set by using method TextAnalyticsClientBuilder.defaultLanguage(String). If none is specified, service will use 'en' as the language.

      Recognize linked entities in a document. Subscribes to the call asynchronously and prints out the entity details when a response is received.

       String document = "Old Faithful is a geyser at Yellowstone Park.";
 textAnalyticsAsyncClient.recognizeLinkedEntities(document).subscribe(
     linkedEntityCollection -> linkedEntityCollection.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(entityMatch -> System.out.printf(
             "Matched entity: %s, confidence score: %f.%n",
             entityMatch.getText(), entityMatch.getConfidenceScore()));
     }));
      Parameters:
      document - The document to recognize linked entities for. For text length limits, maximum batch size, and supported text encoding, see data limits.
      Returns:
      A Mono contains a recognized linked entities collection.
      Throws:
      NullPointerException - if document is null.
      TextAnalyticsException - if the response returned with an error.

    • recognizeLinkedEntities

      public Mono<LinkedEntityCollection> recognizeLinkedEntities(String document, String language)
      Returns a list of recognized entities with links to a well-known knowledge base for the provided document. See this for supported languages in Text Analytics API.

      Recognize linked entities in a text with provided language code. Subscribes to the call asynchronously and prints out the entity details when a response is received.

       String document = "Old Faithful is a geyser at Yellowstone Park.";
 textAnalyticsAsyncClient.recognizeLinkedEntities(document, "en").subscribe(
     linkedEntityCollection -> linkedEntityCollection.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(entityMatch -> System.out.printf(
             "Matched entity: %s, confidence score: %f.%n",
             entityMatch.getText(), entityMatch.getConfidenceScore()));
     }));
      Parameters:
      document - The document to recognize linked entities for. For text length limits, maximum batch size, and supported text encoding, see data limits.
      language - The 2 letter ISO 639-1 representation of language for the document. If not set, uses "en" for English as default.
      Returns:
      A Mono contains a recognized linked entities collection.
      Throws:
      NullPointerException - if document is null.
      TextAnalyticsException - if the response returned with an error.

    • recognizeLinkedEntitiesBatch

      public Mono<RecognizeLinkedEntitiesResultCollection> recognizeLinkedEntitiesBatch(Iterable<String> documents, String language, TextAnalyticsRequestOptions options)
      Returns a list of recognized entities with links to a well-known knowledge base for the list of documents with provided language code and request options. See this for supported languages in Text Analytics API.

      Recognize linked entities in a list of documents with provided language code. Subscribes to the call asynchronously and prints out the entity details when a response is received.

       List<String> documents = Arrays.asList(
     "Old Faithful is a geyser at Yellowstone Park.",
     "Mount Shasta has lenticular clouds."
 );

 textAnalyticsAsyncClient.recognizeLinkedEntitiesBatch(documents, "en", null)
     .subscribe(batchResult -> {
         // Batch statistics
         TextDocumentBatchStatistics batchStatistics = batchResult.getStatistics();
         System.out.printf("Batch statistics, transaction count: %s, valid document count: %s.%n",
             batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());

         batchResult.forEach(recognizeLinkedEntitiesResult ->
             recognizeLinkedEntitiesResult.getEntities().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(entityMatch -> System.out.printf(
                     "Matched entity: %s, confidence score: %f.%n",
                     entityMatch.getText(), entityMatch.getConfidenceScore()));
             }));
     });
      Parameters:
      documents - A list of documents to recognize linked entities for. For text length limits, maximum batch size, and supported text encoding, see data limits.
      language - The 2 letter ISO 639-1 representation of language for the text. If not set, uses "en" for English as default.
      options - The options to configure the scoring model for documents and show statistics.
      Returns:
      A Mono contains a RecognizeLinkedEntitiesResultCollection.
      Throws:
      NullPointerException - if documents is null.

    • recognizeLinkedEntitiesBatchWithResponse

      public Mono<Response<RecognizeLinkedEntitiesResultCollection>> recognizeLinkedEntitiesBatchWithResponse(Iterable<TextDocumentInput> documents, TextAnalyticsRequestOptions options)
      Returns a list of recognized entities with links to a well-known knowledge base for the list of document with provided request options. See this supported languages in Text Analytics API.

      Recognize linked entities in a list of document and provided request options to show statistics. Subscribes to the call asynchronously and prints out the entity details when a response is received.

       List<TextDocumentInput> textDocumentInputs1 = Arrays.asList(
     new TextDocumentInput("0", "Old Faithful is a geyser at Yellowstone Park.").setLanguage("en"),
     new TextDocumentInput("1", "Mount Shasta has lenticular clouds.").setLanguage("en"));

 TextAnalyticsRequestOptions requestOptions = new TextAnalyticsRequestOptions().setIncludeStatistics(true);

 textAnalyticsAsyncClient.recognizeLinkedEntitiesBatchWithResponse(textDocumentInputs1, requestOptions)
     .subscribe(response -> {
         // Response's status code
         System.out.printf("Status code of request response: %d%n", response.getStatusCode());
         RecognizeLinkedEntitiesResultCollection resultCollection = response.getValue();

         // Batch statistics
         TextDocumentBatchStatistics batchStatistics = resultCollection.getStatistics();
         System.out.printf("Batch statistics, transaction count: %s, valid document count: %s.%n",
             batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());

         resultCollection.forEach(recognizeLinkedEntitiesResult ->
             recognizeLinkedEntitiesResult.getEntities().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(entityMatch -> System.out.printf(
                     "Matched entity: %s, confidence score: %.2f.%n",
                     entityMatch.getText(), entityMatch.getConfidenceScore()));
             }));
     });
      Parameters:
      documents - A list of documents to recognize linked entities for. For text length limits, maximum batch size, and supported text encoding, see data limits.
      options - The options to configure the scoring model for documents and show statistics.
      Returns:
      A Mono contains a Response which contains a RecognizeLinkedEntitiesResultCollection.
      Throws:
      NullPointerException - if documents is null.

    • extractKeyPhrases

      public Mono<KeyPhrasesCollection> extractKeyPhrases(String document)
      Returns a list of strings denoting the key phrases in the document. This method will use the default language that can be set by using method TextAnalyticsClientBuilder.defaultLanguage(String). If none is specified, service will use 'en' as the language.

      Extract key phrases in a document. Subscribes to the call asynchronously and prints out the key phrases when a response is received.

       System.out.println("Extracted phrases:");
 textAnalyticsAsyncClient.extractKeyPhrases("Bonjour tout le monde").subscribe(keyPhrase ->
     System.out.printf("%s.%n", keyPhrase));
      Parameters:
      document - The document to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      Returns:
      A Mono contains a KeyPhrasesCollection.
      Throws:
      NullPointerException - if document is null.
      TextAnalyticsException - if the response returned with an error.

    • extractKeyPhrases

      public Mono<KeyPhrasesCollection> extractKeyPhrases(String document, String language)
      Returns a list of strings denoting the key phrases in the document. See this for the list of enabled languages.

      Extract key phrases in a document with a provided language code. Subscribes to the call asynchronously and prints out the key phrases when a response is received.

       System.out.println("Extracted phrases:");
 textAnalyticsAsyncClient.extractKeyPhrases("Bonjour tout le monde", "fr")
     .subscribe(keyPhrase -> System.out.printf("%s.%n", keyPhrase));
      Parameters:
      document - The document to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      language - The 2 letter ISO 639-1 representation of language for the text. If not set, uses "en" for English as default.
      Returns:
      A Mono contains a KeyPhrasesCollection
      Throws:
      NullPointerException - if document is null.
      TextAnalyticsException - if the response returned with an error.

    • extractKeyPhrasesBatch

      public Mono<ExtractKeyPhrasesResultCollection> extractKeyPhrasesBatch(Iterable<String> documents, String language, TextAnalyticsRequestOptions options)
      Returns a list of strings denoting the key phrases in the document with provided language code and request options. See this for the list of enabled languages.

      Extract key phrases in a list of documents with a provided language and request options. Subscribes to the call asynchronously and prints out the key phrases when a response is received.

       List<String> documents = Arrays.asList(
     "Hello world. This is some input text that I love.",
     "Bonjour tout le monde");

 textAnalyticsAsyncClient.extractKeyPhrasesBatch(documents, "en", null).subscribe(
     extractKeyPhraseResults -> {
         // Batch statistics
         TextDocumentBatchStatistics batchStatistics = extractKeyPhraseResults.getStatistics();
         System.out.printf("Batch statistics, transaction count: %s, valid document count: %s.%n",
             batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());

         extractKeyPhraseResults.forEach(extractKeyPhraseResult -> {
             System.out.println("Extracted phrases:");
             extractKeyPhraseResult.getKeyPhrases().forEach(keyPhrase -> System.out.printf("%s.%n", keyPhrase));
         });
     });
      Parameters:
      documents - A list of documents to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      language - The 2 letter ISO 639-1 representation of language for the text. If not set, uses "en" for English as default.
      options - The options to configure the scoring model for documents and show statistics.
      Returns:
      A Mono contains a ExtractKeyPhrasesResultCollection.
      Throws:
      NullPointerException - if documents is null.

    • extractKeyPhrasesBatchWithResponse

      public Mono<Response<ExtractKeyPhrasesResultCollection>> extractKeyPhrasesBatchWithResponse(Iterable<TextDocumentInput> documents, TextAnalyticsRequestOptions options)
      Returns a list of strings denoting the key phrases in the document with provided request options. See this for the list of enabled languages.

      Extract key phrases in a list of document with provided request options. Subscribes to the call asynchronously and prints out the key phrases when a response is received.

       List<TextDocumentInput> textDocumentInputs1 = Arrays.asList(
     new TextDocumentInput("0", "I had a wonderful trip to Seattle last week.").setLanguage("en"),
     new TextDocumentInput("1", "I work at Microsoft.").setLanguage("en"));

 TextAnalyticsRequestOptions requestOptions = new TextAnalyticsRequestOptions().setIncludeStatistics(true);

 textAnalyticsAsyncClient.extractKeyPhrasesBatchWithResponse(textDocumentInputs1, requestOptions)
     .subscribe(response -> {
         // Response's status code
         System.out.printf("Status code of request response: %d%n", response.getStatusCode());
         ExtractKeyPhrasesResultCollection resultCollection = response.getValue();

         // Batch statistics
         TextDocumentBatchStatistics batchStatistics = resultCollection.getStatistics();
         System.out.printf("Batch statistics, transaction count: %s, valid document count: %s.%n",
             batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());

         for (ExtractKeyPhraseResult extractKeyPhraseResult : resultCollection) {
             System.out.println("Extracted phrases:");
             for (String keyPhrase : extractKeyPhraseResult.getKeyPhrases()) {
                 System.out.printf("%s.%n", keyPhrase);
             }
         }
     });
      Parameters:
      documents - A list of documents to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      options - The options to configure the scoring model for documents and show statistics.
      Returns:
      A Mono contains a Response that contains a ExtractKeyPhrasesResultCollection.
      Throws:
      NullPointerException - if documents is null.

    • analyzeSentiment

      public Mono<DocumentSentiment> analyzeSentiment(String document)
      Returns a sentiment prediction, as well as confidence scores for each sentiment label (Positive, Negative, and Neutral) for the document and each sentence within it. This method will use the default language that can be set by using method TextAnalyticsClientBuilder.defaultLanguage(String). If none is specified, service will use 'en' as the language.

      Code Sample

      Analyze the sentiment in a document. Subscribes to the call asynchronously and prints out the sentiment details when a response is received.

       String document = "The hotel was dark and unclean.";
 textAnalyticsAsyncClient.analyzeSentiment(document).subscribe(documentSentiment -> {
     System.out.printf("Recognized document sentiment: %s.%n", documentSentiment.getSentiment());

     for (SentenceSentiment sentenceSentiment : documentSentiment.getSentences()) {
         System.out.printf(
             "Recognized sentence sentiment: %s, positive score: %.2f, neutral score: %.2f, "
                 + "negative score: %.2f.%n",
             sentenceSentiment.getSentiment(),
             sentenceSentiment.getConfidenceScores().getPositive(),
             sentenceSentiment.getConfidenceScores().getNeutral(),
             sentenceSentiment.getConfidenceScores().getNegative());
     }
 });
      Parameters:
      document - The document to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      Returns:
      A Mono contains the analyzed document sentiment of the document.
      Throws:
      NullPointerException - if document is null.
      TextAnalyticsException - if the response returned with an error.

    • analyzeSentiment

      public Mono<DocumentSentiment> analyzeSentiment(String document, String language)
      Returns a sentiment prediction, as well as confidence scores for each sentiment label (Positive, Negative, and Neutral) for the document and each sentence within it.

      Code Sample

      Analyze the sentiments in a document with a provided language representation. Subscribes to the call asynchronously and prints out the sentiment details when a response is received.

       String document = "The hotel was dark and unclean.";
 textAnalyticsAsyncClient.analyzeSentiment(document, "en")
     .subscribe(documentSentiment -> {
         System.out.printf("Recognized sentiment label: %s.%n", documentSentiment.getSentiment());
         for (SentenceSentiment sentenceSentiment : documentSentiment.getSentences()) {
             System.out.printf("Recognized sentence sentiment: %s, positive score: %.2f, neutral score: %.2f, "
                     + "negative score: %.2f.%n",
                 sentenceSentiment.getSentiment(),
                 sentenceSentiment.getConfidenceScores().getPositive(),
                 sentenceSentiment.getConfidenceScores().getNeutral(),
                 sentenceSentiment.getConfidenceScores().getNegative());
         }
     });
      Parameters:
      document - The document to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      language - The 2 letter ISO 639-1 representation of language for the text. If not set, uses "en" for English as default.
      Returns:
      A Mono contains the analyzed document sentiment of the document.
      Throws:
      NullPointerException - if document is null.
      TextAnalyticsException - if the response returned with an error.

    • analyzeSentiment

      public Mono<DocumentSentiment> analyzeSentiment(String document, String language, AnalyzeSentimentOptions options)
      Returns a sentiment prediction, as well as confidence scores for each sentiment label (Positive, Negative, and Neutral) for the document and each sentence within it. If the includeOpinionMining of AnalyzeSentimentOptions set to true, the output will include the opinion mining results. It mines the opinions of a sentence and conducts more granular analysis around the aspects in the text (also known as aspect-based sentiment analysis).

      Code Sample

      Analyze the sentiment and mine the opinions for each sentence in a document with a provided language representation and AnalyzeSentimentOptions options. Subscribes to the call asynchronously and prints out the sentiment and sentence opinions details when a response is received.

       textAnalyticsAsyncClient.analyzeSentiment("The hotel was dark and unclean.", "en",
     new AnalyzeSentimentOptions().setIncludeOpinionMining(true))
     .subscribe(documentSentiment -> {
         for (SentenceSentiment sentenceSentiment : documentSentiment.getSentences()) {
             System.out.printf("\tSentence sentiment: %s%n", sentenceSentiment.getSentiment());
             sentenceSentiment.getOpinions().forEach(opinion -> {
                 TargetSentiment targetSentiment = opinion.getTarget();
                 System.out.printf("\tTarget sentiment: %s, target text: %s%n",
                     targetSentiment.getSentiment(), targetSentiment.getText());
                 for (AssessmentSentiment assessmentSentiment : opinion.getAssessments()) {
                     System.out.printf("\t\t'%s' sentiment because of \"%s\". Is the assessment negated: %s.%n",
                         assessmentSentiment.getSentiment(), assessmentSentiment.getText(),
                         assessmentSentiment.isNegated());
                 }
             });
         }
     });
      Parameters:
      document - The document to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      language - The 2 letter ISO 639-1 representation of language for the text. If not set, uses "en" for English as default.
      options - The additional configurable options that may be passed when analyzing sentiments.
      Returns:
      A Mono contains the analyzed document sentiment of the document.
      Throws:
      NullPointerException - if document is null.
      TextAnalyticsException - if the response returned with an error.

    • analyzeSentimentBatch

      @Deprecated public Mono<AnalyzeSentimentResultCollection> analyzeSentimentBatch(Iterable<String> documents, String language, TextAnalyticsRequestOptions options)
      Deprecated.
      Please use the analyzeSentimentBatch(Iterable, String, AnalyzeSentimentOptions).
      Returns a sentiment prediction, as well as confidence scores for each sentiment label (Positive, Negative, and Neutral) for the document and each sentence within it.

      Analyze sentiment in a list of documents with provided language code and request options. Subscribes to the call asynchronously and prints out the sentiment details when a response is received.

       List<String> documents = Arrays.asList(
     "The hotel was dark and unclean.",
     "The restaurant had amazing gnocchi."
 );

 textAnalyticsAsyncClient.analyzeSentimentBatch(documents, "en",
     new TextAnalyticsRequestOptions().setIncludeStatistics(true)).subscribe(
         response -> {
             // Batch statistics
             TextDocumentBatchStatistics batchStatistics = response.getStatistics();
             System.out.printf("Batch statistics, transaction count: %s, valid document count: %s.%n",
                 batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());

             response.forEach(analyzeSentimentResult -> {
                 System.out.printf("Document ID: %s%n", analyzeSentimentResult.getId());
                 DocumentSentiment documentSentiment = analyzeSentimentResult.getDocumentSentiment();
                 System.out.printf("Recognized document sentiment: %s.%n", documentSentiment.getSentiment());
                 documentSentiment.getSentences().forEach(sentenceSentiment ->
                     System.out.printf("Recognized sentence sentiment: %s, positive score: %.2f, "
                             + "neutral score: %.2f, negative score: %.2f.%n",
                         sentenceSentiment.getSentiment(),
                         sentenceSentiment.getConfidenceScores().getPositive(),
                         sentenceSentiment.getConfidenceScores().getNeutral(),
                         sentenceSentiment.getConfidenceScores().getNegative()));
             });
         });
      Parameters:
      documents - A list of documents to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      language - The 2 letter ISO 639-1 representation of language for the document. If not set, uses "en" for English as default.
      options - The options to configure the scoring model for documents and show statistics.
      Returns:
      A Mono contains a AnalyzeSentimentResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.

    • analyzeSentimentBatch

      public Mono<AnalyzeSentimentResultCollection> analyzeSentimentBatch(Iterable<String> documents, String language, AnalyzeSentimentOptions options)
      Returns a sentiment prediction, as well as confidence scores for each sentiment label (Positive, Negative, and Neutral) for the document and each sentence within it. If the includeOpinionMining of AnalyzeSentimentOptions set to true, the output will include the opinion mining results. It mines the opinions of a sentence and conducts more granular analysis around the aspects in the text (also known as aspect-based sentiment analysis).

      Code Sample

      Analyze the sentiments and mine the opinions for each sentence in a list of documents with a provided language representation and AnalyzeSentimentOptions options. Subscribes to the call asynchronously and prints out the sentiment and sentence opinions details when a response is received.

       List<TextDocumentInput> documents = Arrays.asList(
     new TextDocumentInput("0", "Elon Musk is the CEO of SpaceX and Tesla.").setLanguage("en"),
     new TextDocumentInput("1", "My SSN is 859-98-0987").setLanguage("en")
 );

 SyncPoller<AnalyzeActionsOperationDetail, AnalyzeActionsResultPagedIterable> syncPoller =
     textAnalyticsClient.beginAnalyzeActions(
         documents,
         new TextAnalyticsActions().setDisplayName("{tasks_display_name}")
            .setRecognizeEntitiesActions(new RecognizeEntitiesAction())
            .setExtractKeyPhrasesActions(new ExtractKeyPhrasesAction()),
         new AnalyzeActionsOptions().setIncludeStatistics(false),
         Context.NONE);
 syncPoller.waitForCompletion();
 AnalyzeActionsResultPagedIterable result = syncPoller.getFinalResult();
 result.forEach(analyzeActionsResult -> {
     System.out.println("Entities recognition action results:");
     analyzeActionsResult.getRecognizeEntitiesResults().forEach(
         actionResult -> {
             if (!actionResult.isError()) {
                 actionResult.getDocumentsResults().forEach(
                     entitiesResult -> entitiesResult.getEntities().forEach(
                         entity -> System.out.printf(
                             "Recognized entity: %s, entity category: %s, entity subcategory: %s,"
                                 + " confidence score: %f.%n",
                             entity.getText(), entity.getCategory(), entity.getSubcategory(),
                             entity.getConfidenceScore())));
             }
         });
     System.out.println("Key phrases extraction action results:");
     analyzeActionsResult.getExtractKeyPhrasesResults().forEach(
         actionResult -> {
             if (!actionResult.isError()) {
                 actionResult.getDocumentsResults().forEach(extractKeyPhraseResult -> {
                     System.out.println("Extracted phrases:");
                     extractKeyPhraseResult.getKeyPhrases()
                         .forEach(keyPhrases -> System.out.printf("\t%s.%n", keyPhrases));
                 });
             }
         });
 });
      Parameters:
      documents - A list of documents to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      language - The 2 letter ISO 639-1 representation of language for the document. If not set, uses "en" for English as default.
      options - The additional configurable options that may be passed when analyzing sentiments.
      Returns:
      A Mono contains a AnalyzeSentimentResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.

    • analyzeSentimentBatchWithResponse

      @Deprecated public Mono<Response<AnalyzeSentimentResultCollection>> analyzeSentimentBatchWithResponse(Iterable<TextDocumentInput> documents, TextAnalyticsRequestOptions options)
      Deprecated.
      Please use the analyzeSentimentBatchWithResponse(Iterable, AnalyzeSentimentOptions).
      Returns a sentiment prediction, as well as confidence scores for each sentiment label (Positive, Negative, and Neutral) for the document and each sentence within it.

      Analyze sentiment in a list of document with provided request options. Subscribes to the call asynchronously and prints out the sentiment details when a response is received.

       List<TextDocumentInput> textDocumentInputs1 = Arrays.asList(
     new TextDocumentInput("0", "The hotel was dark and unclean.").setLanguage("en"),
     new TextDocumentInput("1", "The restaurant had amazing gnocchi.").setLanguage("en"));

 TextAnalyticsRequestOptions requestOptions = new TextAnalyticsRequestOptions().setIncludeStatistics(true);

 textAnalyticsAsyncClient.analyzeSentimentBatchWithResponse(textDocumentInputs1, requestOptions)
     .subscribe(response -> {
         // Response's status code
         System.out.printf("Status code of request response: %d%n", response.getStatusCode());
         AnalyzeSentimentResultCollection resultCollection = response.getValue();

         // Batch statistics
         TextDocumentBatchStatistics batchStatistics = resultCollection.getStatistics();
         System.out.printf("Batch statistics, transaction count: %s, valid document count: %s.%n",
             batchStatistics.getTransactionCount(),
             batchStatistics.getValidDocumentCount());

         resultCollection.forEach(analyzeSentimentResult -> {
             System.out.printf("Document ID: %s%n", analyzeSentimentResult.getId());
             DocumentSentiment documentSentiment = analyzeSentimentResult.getDocumentSentiment();
             System.out.printf("Recognized document sentiment: %s.%n", documentSentiment.getSentiment());
             documentSentiment.getSentences().forEach(sentenceSentiment ->
                 System.out.printf("Recognized sentence sentiment: %s, positive score: %.2f, "
                         + "neutral score: %.2f, negative score: %.2f.%n",
                     sentenceSentiment.getSentiment(),
                     sentenceSentiment.getConfidenceScores().getPositive(),
                     sentenceSentiment.getConfidenceScores().getNeutral(),
                     sentenceSentiment.getConfidenceScores().getNegative()));
         });
     });
      Parameters:
      documents - A list of documents to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      options - The options to configure the scoring model for documents and show statistics.
      Returns:
      A Mono contains a Response that contains a AnalyzeSentimentResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.

    • analyzeSentimentBatchWithResponse

      public Mono<Response<AnalyzeSentimentResultCollection>> analyzeSentimentBatchWithResponse(Iterable<TextDocumentInput> documents, AnalyzeSentimentOptions options)
      Returns a sentiment prediction, as well as confidence scores for each sentiment label (Positive, Negative, and Neutral) for the document and each sentence within it. If the includeOpinionMining of AnalyzeSentimentOptions set to true, the output will include the opinion mining results. It mines the opinions of a sentence and conducts more granular analysis around the aspects in the text (also known as aspect-based sentiment analysis).

      Code Sample

      Analyze sentiment and mine the opinions for each sentence in a list of document with provided AnalyzeSentimentOptions options. Subscribes to the call asynchronously and prints out the sentiment and sentence opinions details when a response is received.

       List<TextDocumentInput> textDocumentInputs1 = Arrays.asList(
     new TextDocumentInput("0", "The hotel was dark and unclean.").setLanguage("en"),
     new TextDocumentInput("1", "The restaurant had amazing gnocchi.").setLanguage("en"));

 AnalyzeSentimentOptions options = new AnalyzeSentimentOptions()
     .setIncludeOpinionMining(true).setIncludeStatistics(true);
 textAnalyticsAsyncClient.analyzeSentimentBatchWithResponse(textDocumentInputs1, options)
     .subscribe(response -> {
         // Response's status code
         System.out.printf("Status code of request response: %d%n", response.getStatusCode());
         AnalyzeSentimentResultCollection resultCollection = response.getValue();

         // Batch statistics
         TextDocumentBatchStatistics batchStatistics = resultCollection.getStatistics();
         System.out.printf("Batch statistics, transaction count: %s, valid document count: %s.%n",
             batchStatistics.getTransactionCount(),
             batchStatistics.getValidDocumentCount());

         resultCollection.forEach(analyzeSentimentResult -> {
             System.out.printf("Document ID: %s%n", analyzeSentimentResult.getId());
             DocumentSentiment documentSentiment = analyzeSentimentResult.getDocumentSentiment();
             documentSentiment.getSentences().forEach(sentenceSentiment -> {
                 System.out.printf("\tSentence sentiment: %s%n", sentenceSentiment.getSentiment());
                 sentenceSentiment.getOpinions().forEach(opinion -> {
                     TargetSentiment targetSentiment = opinion.getTarget();
                     System.out.printf("\t\tTarget sentiment: %s, target text: %s%n",
                         targetSentiment.getSentiment(), targetSentiment.getText());
                     for (AssessmentSentiment assessmentSentiment : opinion.getAssessments()) {
                         System.out.printf(
                             "\t\t\t'%s' assessment sentiment because of \"%s\". Is the assessment negated: %s.%n",
                             assessmentSentiment.getSentiment(), assessmentSentiment.getText(),
                             assessmentSentiment.isNegated());
                     }
                 });
             });
         });
     });
      Parameters:
      documents - A list of documents to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      options - The additional configurable options that may be passed when analyzing sentiments.
      Returns:
      A Mono contains a Response that contains a AnalyzeSentimentResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.

    • beginAnalyzeHealthcareEntities

      public PollerFlux<AnalyzeHealthcareEntitiesOperationDetail,AnalyzeHealthcareEntitiesPagedFlux> beginAnalyzeHealthcareEntities(Iterable<String> documents, String language, AnalyzeHealthcareEntitiesOptions options)
      Analyze healthcare entities, entity data sources, and entity relations in a list of documents with provided request options. Note: In order to use this functionality, request to access public preview is required. Azure Active Directory (AAD) is not currently supported. For more information see this. See this supported languages in Text Analytics API.
      Parameters:
      documents - A list of documents to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      language - The 2-letter ISO 639-1 representation of language for the documents. If not set, uses "en" for English as default.
      options - The additional configurable options that may be passed when analyzing healthcare entities.
      Returns:
      A PollerFlux that polls the analyze healthcare operation until it has completed, has failed, or has been cancelled. The completed operation returns a PagedFlux of AnalyzeHealthcareEntitiesResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
      TextAnalyticsException - If analyze operation fails.

    • beginAnalyzeHealthcareEntities

      public PollerFlux<AnalyzeHealthcareEntitiesOperationDetail,AnalyzeHealthcareEntitiesPagedFlux> beginAnalyzeHealthcareEntities(Iterable<TextDocumentInput> documents, AnalyzeHealthcareEntitiesOptions options)
      Analyze healthcare entities, entity data sources, and entity relations in a list of documents with provided request options. Note: In order to use this functionality, request to access public preview is required. Azure Active Directory (AAD) is not currently supported. For more information see this. See this supported languages in Text Analytics API.

      Analyze healthcare entities, entity data sources, and entity relations in a list of document and provided request options to show statistics. Subscribes to the call asynchronously and prints out the entity details when a response is received.

       List<TextDocumentInput> documents = new ArrayList<>();
 for (int i = 0; i < 3; i++) {
     documents.add(new TextDocumentInput(Integer.toString(i),
         "The patient is a 54-year-old gentleman with a history of progressive angina "
             + "over the past several months."));
 }

 AnalyzeHealthcareEntitiesOptions options = new AnalyzeHealthcareEntitiesOptions()
     .setIncludeStatistics(true);

 textAnalyticsAsyncClient.beginAnalyzeHealthcareEntities(documents, options)
     .flatMap(pollResult -> {
         AnalyzeHealthcareEntitiesOperationDetail operationResult = pollResult.getValue();
         System.out.printf("Operation created time: %s, expiration time: %s.%n",
             operationResult.getCreatedAt(), operationResult.getExpiresAt());
         return pollResult.getFinalResult();
     })
     .flatMap(analyzeActionsResultPagedFlux -> analyzeActionsResultPagedFlux.byPage())
     .subscribe(
         pagedResponse -> pagedResponse.getElements().forEach(
             analyzeHealthcareEntitiesResultCollection -> {
                 // Model version
                 System.out.printf("Results of Azure Text Analytics \"Analyze Healthcare\" Model, version: %s%n",
                     analyzeHealthcareEntitiesResultCollection.getModelVersion());

                 TextDocumentBatchStatistics healthcareTaskStatistics =
                     analyzeHealthcareEntitiesResultCollection.getStatistics();
                 // Batch statistics
                 System.out.printf("Documents statistics: document count = %s, erroneous document count = %s,"
                                       + " transaction count = %s, valid document count = %s.%n",
                     healthcareTaskStatistics.getDocumentCount(),
                     healthcareTaskStatistics.getInvalidDocumentCount(),
                     healthcareTaskStatistics.getTransactionCount(),
                     healthcareTaskStatistics.getValidDocumentCount());

                 analyzeHealthcareEntitiesResultCollection.forEach(healthcareEntitiesResult -> {
                     System.out.println("document id = " + healthcareEntitiesResult.getId());
                     System.out.println("Document entities: ");
                     AtomicInteger ct = new AtomicInteger();
                     healthcareEntitiesResult.getEntities().forEach(healthcareEntity -> {
                         System.out.printf(
                             "\ti = %d, Text: %s, category: %s, confidence score: %f.%n",
                             ct.getAndIncrement(), healthcareEntity.getText(), healthcareEntity.getCategory(),
                             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());
                         });
                     });
                 });
             }));
      Parameters:
      documents - A list of documents to be analyzed.
      options - The additional configurable options that may be passed when analyzing healthcare entities.
      Returns:
      A PollerFlux that polls the analyze healthcare operation until it has completed, has failed, or has been cancelled. The completed operation returns a PagedFlux of AnalyzeHealthcareEntitiesResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
      TextAnalyticsException - If analyze operation fails.

    • beginAnalyzeActions

      public PollerFlux<AnalyzeActionsOperationDetail,AnalyzeActionsResultPagedFlux> beginAnalyzeActions(Iterable<String> documents, TextAnalyticsActions actions, String language, AnalyzeActionsOptions options)
      Execute actions, such as, entities recognition, PII entities recognition and key phrases extraction for a list of documents with provided request options. See this supported languages in Text Analytics API.

      Code Sample

       List<String> documents = Arrays.asList(
     "Elon Musk is the CEO of SpaceX and Tesla.",
     "1", "My SSN is 859-98-0987"
 );
 textAnalyticsAsyncClient.beginAnalyzeActions(documents,
     new TextAnalyticsActions().setDisplayName("{tasks_display_name}")
         .setRecognizeEntitiesActions(new RecognizeEntitiesAction())
         .setExtractKeyPhrasesActions(new ExtractKeyPhrasesAction()),
     "en",
     new AnalyzeActionsOptions().setIncludeStatistics(false))
     .flatMap(AsyncPollResponse::getFinalResult)
     .flatMap(analyzeActionsResultPagedFlux -> analyzeActionsResultPagedFlux.byPage())
     .subscribe(
         pagedResponse -> pagedResponse.getElements().forEach(
             analyzeActionsResult -> {
                 analyzeActionsResult.getRecognizeEntitiesResults().forEach(
                     actionResult -> {
                         if (!actionResult.isError()) {
                             actionResult.getDocumentsResults().forEach(
                                 entitiesResult -> entitiesResult.getEntities().forEach(
                                     entity -> System.out.printf(
                                         "Recognized entity: %s, entity category: %s, entity subcategory: %s,"
                                             + " confidence score: %f.%n",
                                         entity.getText(), entity.getCategory(), entity.getSubcategory(),
                                         entity.getConfidenceScore())));
                         }
                     });
                 analyzeActionsResult.getExtractKeyPhrasesResults().forEach(
                     actionResult -> {
                         if (!actionResult.isError()) {
                             actionResult.getDocumentsResults().forEach(extractKeyPhraseResult -> {
                                 System.out.println("Extracted phrases:");
                                 extractKeyPhraseResult.getKeyPhrases()
                                     .forEach(keyPhrases -> System.out.printf("\t%s.%n", keyPhrases));
                             });
                         }
                     });
             }));
      Parameters:
      documents - A list of documents to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.
      actions - The actions that contains all actions to be executed. An action is one task of execution, such as a single task of 'Key Phrases Extraction' on the given document inputs.
      language - The 2 letter ISO 639-1 representation of language for the documents. If not set, uses "en" for English as default.
      options - The additional configurable options that may be passed when analyzing a collection of actions.
      Returns:
      A PollerFlux that polls the analyze a collection of actions operation until it has completed, has failed, or has been cancelled. The completed operation returns a AnalyzeActionsResultPagedFlux.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
      TextAnalyticsException - If analyze operation fails.

    • beginAnalyzeActions

      public PollerFlux<AnalyzeActionsOperationDetail,AnalyzeActionsResultPagedFlux> beginAnalyzeActions(Iterable<TextDocumentInput> documents, TextAnalyticsActions actions, AnalyzeActionsOptions options)
      Execute actions, such as, entities recognition, PII entities recognition and key phrases extraction for a list of documents with provided request options. See this supported languages in Text Analytics API.

      Code Sample

       List<TextDocumentInput> documents = Arrays.asList(
     new TextDocumentInput("0", "Elon Musk is the CEO of SpaceX and Tesla.").setLanguage("en"),
     new TextDocumentInput("1", "My SSN is 859-98-0987").setLanguage("en")
 );
 textAnalyticsAsyncClient.beginAnalyzeActions(documents,
     new TextAnalyticsActions().setDisplayName("{tasks_display_name}")
         .setRecognizeEntitiesActions(new RecognizeEntitiesAction())
         .setExtractKeyPhrasesActions(new ExtractKeyPhrasesAction()),
     new AnalyzeActionsOptions().setIncludeStatistics(false))
     .flatMap(AsyncPollResponse::getFinalResult)
     .flatMap(analyzeActionsResultPagedFlux -> analyzeActionsResultPagedFlux.byPage())
     .subscribe(
         pagedResponse -> pagedResponse.getElements().forEach(
             analyzeActionsResult -> {
                 System.out.println("Entities recognition action results:");
                 analyzeActionsResult.getRecognizeEntitiesResults().forEach(
                     actionResult -> {
                         if (!actionResult.isError()) {
                             actionResult.getDocumentsResults().forEach(
                                 entitiesResult -> entitiesResult.getEntities().forEach(
                                     entity -> System.out.printf(
                                         "Recognized entity: %s, entity category: %s, entity subcategory: %s,"
                                             + " confidence score: %f.%n",
                                         entity.getText(), entity.getCategory(), entity.getSubcategory(),
                                         entity.getConfidenceScore())));
                         }
                     });
                 System.out.println("Key phrases extraction action results:");
                 analyzeActionsResult.getExtractKeyPhrasesResults().forEach(
                     actionResult -> {
                         if (!actionResult.isError()) {
                             actionResult.getDocumentsResults().forEach(extractKeyPhraseResult -> {
                                 System.out.println("Extracted phrases:");
                                 extractKeyPhraseResult.getKeyPhrases()
                                     .forEach(keyPhrases -> System.out.printf("\t%s.%n", keyPhrases));
                             });
                         }
                     });
             }));
      Parameters:
      documents - A list of documents to be analyzed.
      actions - The actions that contains all actions to be executed. An action is one task of execution, such as a single task of 'Key Phrases Extraction' on the given document inputs.
      options - The additional configurable options that may be passed when analyzing a collection of tasks.
      Returns:
      A PollerFlux that polls the analyze a collection of tasks operation until it has completed, has failed, or has been cancelled. The completed operation returns a AnalyzeActionsResultPagedFlux.
      Throws:
      NullPointerException - if documents or actions is null.
      IllegalArgumentException - if documents is empty.
      TextAnalyticsException - If analyze operation fails.