Class TextAnalyticsClient

java.lang.Object
com.azure.ai.textanalytics.TextAnalyticsClient

public final class TextAnalyticsClient extends Object
This class provides a synchronous 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 a synchronous Text Analytics Client

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

 SyncPoller<AnalyzeActionsOperationDetail, AnalyzeActionsResultPagedIterable> syncPoller =
     textAnalyticsClient.beginAnalyzeActions(
         documents,
         new TextAnalyticsActions().setDisplayName("{tasks_display_name}")
             .setRecognizeEntitiesActions(new RecognizeEntitiesAction())
             .setExtractKeyPhrasesActions(new ExtractKeyPhrasesAction()),
         "en",
         new AnalyzeActionsOptions().setIncludeStatistics(false));
 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));
                 });
             }
         });
 });
 

View this 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 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 the language of single document.

       DetectedLanguage detectedLanguage = textAnalyticsClient.detectLanguage("Bonjour tout le monde");
       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:
      The detected language of the document.
      Throws:
      NullPointerException - if document is null.
    • detectLanguage

      public DetectedLanguage detectLanguage(String document, String countryHint)
      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.

      Code Sample

      Detects the language of documents with a provided country hint.

       DetectedLanguage detectedLanguage = textAnalyticsClient.detectLanguage(
           "This text is in English", "US");
       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 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".
      Returns:
      The detected language of the document.
      Throws:
      NullPointerException - if document is null.
    • detectLanguageBatch

      public DetectLanguageResultCollection detectLanguageBatch(Iterable<String> documents, String countryHint, TextAnalyticsRequestOptions options)
      Detects Language for a batch of document with the provided country hint and request options.

      Code Sample

      Detects the language in a list of documents with a provided country hint and request options.

       List<String> documents = Arrays.asList(
           "This is written in English",
           "Este es un documento  escrito en Español."
       );
      
       DetectLanguageResultCollection resultCollection =
           textAnalyticsClient.detectLanguageBatch(documents, "US", null);
      
       // Batch statistics
       TextDocumentBatchStatistics batchStatistics = resultCollection.getStatistics();
       System.out.printf("A batch of documents statistics, transaction count: %s, valid document count: %s.%n",
           batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());
      
       // Batch result of languages
       resultCollection.forEach(detectLanguageResult -> {
           System.out.printf("Document ID: %s%n", detectLanguageResult.getId());
           DetectedLanguage detectedLanguage = detectLanguageResult.getPrimaryLanguage();
           System.out.printf("Primary 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 DetectLanguageResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
    • detectLanguageBatchWithResponse

      public Response<DetectLanguageResultCollection> detectLanguageBatchWithResponse(Iterable<DetectLanguageInput> documents, TextAnalyticsRequestOptions options, Context context)
      Detects Language for a batch of document with provided request options.

      Code Sample

      Detects the languages with http response in a list of document with provided request options.

       List<DetectLanguageInput> detectLanguageInputs = Arrays.asList(
           new DetectLanguageInput("1", "This is written in English.", "US"),
           new DetectLanguageInput("2", "Este es un documento  escrito en Español.", "es")
       );
      
       Response<DetectLanguageResultCollection> response =
           textAnalyticsClient.detectLanguageBatchWithResponse(detectLanguageInputs,
               new TextAnalyticsRequestOptions().setIncludeStatistics(true), Context.NONE);
      
       // Response's status code
       System.out.printf("Status code of request response: %d%n", response.getStatusCode());
       DetectLanguageResultCollection detectedLanguageResultCollection = response.getValue();
      
       // Batch statistics
       TextDocumentBatchStatistics batchStatistics = detectedLanguageResultCollection.getStatistics();
       System.out.printf(
           "Documents statistics: document count = %s, erroneous document count = %s, transaction count = %s,"
               + " valid document count = %s.%n",
           batchStatistics.getDocumentCount(), batchStatistics.getInvalidDocumentCount(),
           batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());
      
       // Batch result of languages
       detectedLanguageResultCollection.forEach(detectLanguageResult -> {
           System.out.printf("Document ID: %s%n", detectLanguageResult.getId());
           DetectedLanguage detectedLanguage = detectLanguageResult.getPrimaryLanguage();
           System.out.printf("Primary 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.
      context - Additional context that is passed through the Http pipeline during the service call.
      Returns:
      A Response that contains a DetectLanguageResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
    • recognizeEntities

      public CategorizedEntityCollection recognizeEntities(String document)
      Returns a list of general categorized entities in the provided document. For a list of supported entity types, 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 the entities of documents

       final CategorizedEntityCollection recognizeEntitiesResult =
           textAnalyticsClient.recognizeEntities("Satya Nadella is the CEO of Microsoft");
       for (CategorizedEntity entity : recognizeEntitiesResult) {
           System.out.printf("Recognized entity: %s, entity 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 CategorizedEntityCollection contains a list of recognized categorized entities and warnings.
      Throws:
      NullPointerException - if document is null.
      TextAnalyticsException - if the response returned with an error.
    • recognizeEntities

      public CategorizedEntityCollection recognizeEntities(String document, String language)
      Returns a list of general categorized 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

      Recognizes the entities in a document with a provided language code.

       final CategorizedEntityCollection recognizeEntitiesResult =
           textAnalyticsClient.recognizeEntities("Satya Nadella is the CEO of Microsoft", "en");
      
       for (CategorizedEntity entity : recognizeEntitiesResult) {
           System.out.printf("Recognized entity: %s, entity 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.
      language - The 2 letter ISO 639-1 representation of language. If not set, uses "en" for English as default.
      Returns:
      The CategorizedEntityCollection contains a list of recognized categorized entities and warnings.
      Throws:
      NullPointerException - if document is null.
      TextAnalyticsException - if the response returned with an error.
    • recognizeEntitiesBatch

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

      Code Sample

      Recognizes the entities in a list of documents with a provided language code and request options.

       List<String> documents = Arrays.asList(
           "I had a wonderful trip to Seattle last week.",
           "I work at Microsoft.");
      
       RecognizeEntitiesResultCollection resultCollection =
           textAnalyticsClient.recognizeEntitiesBatch(documents, "en", null);
      
       // Batch statistics
       TextDocumentBatchStatistics batchStatistics = resultCollection.getStatistics();
       System.out.printf(
           "A batch of documents statistics, transaction count: %s, valid document count: %s.%n",
           batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());
      
       resultCollection.forEach(recognizeEntitiesResult ->
           recognizeEntitiesResult.getEntities().forEach(entity ->
               System.out.printf("Recognized entity: %s, entity 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 RecognizeEntitiesResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
    • recognizeEntitiesBatchWithResponse

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

      Code Sample

      Recognizes the entities with http response in a list of document with provided request options.

       List<TextDocumentInput> textDocumentInputs = 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")
       );
      
       Response<RecognizeEntitiesResultCollection> response =
           textAnalyticsClient.recognizeEntitiesBatchWithResponse(textDocumentInputs,
               new TextAnalyticsRequestOptions().setIncludeStatistics(true), Context.NONE);
      
       // Response's status code
       System.out.printf("Status code of request response: %d%n", response.getStatusCode());
       RecognizeEntitiesResultCollection recognizeEntitiesResultCollection = response.getValue();
      
       // Batch statistics
       TextDocumentBatchStatistics batchStatistics = recognizeEntitiesResultCollection.getStatistics();
       System.out.printf(
           "A batch of documents statistics, transaction count: %s, valid document count: %s.%n",
           batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());
      
       recognizeEntitiesResultCollection.forEach(recognizeEntitiesResult ->
           recognizeEntitiesResult.getEntities().forEach(entity ->
               System.out.printf("Recognized entity: %s, entity 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.
      context - Additional context that is passed through the Http pipeline during the service call.
      Returns:
      A Response that contains a RecognizeEntitiesResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
    • recognizePiiEntities

      public 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.

       PiiEntityCollection piiEntityCollection = textAnalyticsClient.recognizePiiEntities("My SSN is 859-98-0987");
       System.out.printf("Redacted Text: %s%n", piiEntityCollection.getRedactedText());
       for (PiiEntity entity : piiEntityCollection) {
           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 recognized PII entities collection.
      Throws:
      NullPointerException - if document is null.
      TextAnalyticsException - if the response returned with an error.
    • recognizePiiEntities

      public 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

      Recognizes the PII entities details in a document with a provided language code.

       PiiEntityCollection piiEntityCollection = textAnalyticsClient.recognizePiiEntities(
           "My SSN is 859-98-0987", "en");
       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.
      language - The 2 letter ISO 639-1 representation of language. If not set, uses "en" for English as default.
      Returns:
      The recognized PII entities collection.
      Throws:
      NullPointerException - if document is null.
      TextAnalyticsException - if the response returned with an error.
    • recognizePiiEntities

      public 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

      Recognizes the PII entities details in a document with a provided language code and RecognizePiiEntitiesOptions.

       PiiEntityCollection piiEntityCollection = textAnalyticsClient.recognizePiiEntities(
           "My SSN is 859-98-0987", "en",
           new RecognizePiiEntitiesOptions().setDomainFilter(PiiEntityDomain.PROTECTED_HEALTH_INFORMATION));
       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.
      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:
      The recognized PII entities collection.
      Throws:
      NullPointerException - if document is null.
      TextAnalyticsException - if the response returned with an error.
    • recognizePiiEntitiesBatch

      public 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 provided language code and request options.

      Code Sample

      Recognizes the PII entities details in a list of documents with a provided language code and request options.

       List<String> documents = Arrays.asList(
           "My SSN is 859-98-0987",
           "Visa card 4111 1111 1111 1111"
       );
      
       RecognizePiiEntitiesResultCollection resultCollection = textAnalyticsClient.recognizePiiEntitiesBatch(
           documents, "en", new RecognizePiiEntitiesOptions().setIncludeStatistics(true));
      
       // Batch statistics
       TextDocumentBatchStatistics batchStatistics = resultCollection.getStatistics();
       System.out.printf("A batch of documents statistics, transaction count: %s, valid document count: %s.%n",
           batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());
      
       resultCollection.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 RecognizePiiEntitiesResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
    • recognizePiiEntitiesBatchWithResponse

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

      Code Sample

      Recognizes the PII entities details with http response in a list of document with provided request options.

       List<TextDocumentInput> textDocumentInputs = Arrays.asList(
           new TextDocumentInput("0", "My SSN is 859-98-0987"),
           new TextDocumentInput("1", "Visa card 4111 1111 1111 1111")
       );
      
       Response<RecognizePiiEntitiesResultCollection> response =
           textAnalyticsClient.recognizePiiEntitiesBatchWithResponse(textDocumentInputs,
               new RecognizePiiEntitiesOptions().setIncludeStatistics(true), Context.NONE);
      
       RecognizePiiEntitiesResultCollection resultCollection = response.getValue();
      
       // Batch statistics
       TextDocumentBatchStatistics batchStatistics = resultCollection.getStatistics();
       System.out.printf("A batch of documents statistics, transaction count: %s, valid document count: %s.%n",
           batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());
      
       resultCollection.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.
      context - Additional context that is passed through the Http pipeline during the service call.
      Returns:
      A Response that contains a RecognizePiiEntitiesResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
    • recognizeLinkedEntities

      public 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.

      Code Sample

      Recognize the linked entities of documents

       final String document = "Old Faithful is a geyser at Yellowstone Park.";
       System.out.println("Linked Entities:");
       textAnalyticsClient.recognizeLinkedEntities(document).forEach(linkedEntity -> {
           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 LinkedEntityCollection contains a list of recognized linked entities.
      Throws:
      NullPointerException - if document is null.
      TextAnalyticsException - if the response returned with an error.
    • recognizeLinkedEntities

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

      Code Sample

      Recognizes the linked entities in a document with a provided language code.

       String document = "Old Faithful is a geyser at Yellowstone Park.";
       textAnalyticsClient.recognizeLinkedEntities(document, "en").forEach(linkedEntity -> {
           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 LinkedEntityCollection contains a list of recognized linked entities.
      Throws:
      NullPointerException - if document is null.
      TextAnalyticsException - if the response returned with an error.
    • recognizeLinkedEntitiesBatch

      public 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.

      Code Sample

      Recognizes the linked entities in a list of documents with a provided language code and request options.

       List<String> documents = Arrays.asList(
           "Old Faithful is a geyser at Yellowstone Park.",
           "Mount Shasta has lenticular clouds."
       );
      
       RecognizeLinkedEntitiesResultCollection resultCollection =
           textAnalyticsClient.recognizeLinkedEntitiesBatch(documents, "en", null);
      
       // Batch statistics
       TextDocumentBatchStatistics batchStatistics = resultCollection.getStatistics();
       System.out.printf("A batch of documents 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: %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 documents. If not set, uses "en" for English as default.
      options - The options to configure the scoring model for documents and show statistics.
      Returns:
      A RecognizeLinkedEntitiesResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
    • recognizeLinkedEntitiesBatchWithResponse

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

      Code Sample

      Recognizes the linked entities with http response in a list of TextDocumentInput with request options.

       List<TextDocumentInput> textDocumentInputs = Arrays.asList(
           new TextDocumentInput("1", "Old Faithful is a geyser at Yellowstone Park.").setLanguage("en"),
           new TextDocumentInput("2", "Mount Shasta has lenticular clouds.").setLanguage("en")
       );
      
       Response<RecognizeLinkedEntitiesResultCollection> response =
           textAnalyticsClient.recognizeLinkedEntitiesBatchWithResponse(textDocumentInputs,
               new TextAnalyticsRequestOptions().setIncludeStatistics(true), Context.NONE);
      
       // 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(
           "A batch of documents 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.
      context - Additional context that is passed through the Http pipeline during the service call.
      Returns:
      A Response that contains a RecognizeLinkedEntitiesResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
    • extractKeyPhrases

      public 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.

      Code Sample

      Extracts key phrases of documents

       System.out.println("Extracted phrases:");
       for (String keyPhrase : textAnalyticsClient.extractKeyPhrases("My cat might need to see a veterinarian.")) {
           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 KeyPhrasesCollection contains a list of extracted key phrases.
      Throws:
      NullPointerException - if document is null.
      TextAnalyticsException - if the response returned with an error.
    • extractKeyPhrases

      public 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.

      Code Sample

      Extracts key phrases in a document with a provided language representation.

       System.out.println("Extracted phrases:");
       textAnalyticsClient.extractKeyPhrases("My cat might need to see a veterinarian.", "en")
           .forEach(kegPhrase -> System.out.printf("%s.%n", kegPhrase));
       
      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 document. If not set, uses "en" for English as default.
      Returns:
      A KeyPhrasesCollection contains a list of extracted key phrases.
      Throws:
      NullPointerException - if document is null.
      TextAnalyticsException - if the response returned with an error.
    • extractKeyPhrasesBatch

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

      Code Sample

      Extracts key phrases in a list of documents with a provided language code and request options.

       List<String> documents = Arrays.asList(
           "My cat might need to see a veterinarian.",
           "The pitot tube is used to measure airspeed."
       );
      
       // Extracting batch key phrases
       ExtractKeyPhrasesResultCollection resultCollection =
           textAnalyticsClient.extractKeyPhrasesBatch(documents, "en", null);
      
       // Batch statistics
       TextDocumentBatchStatistics batchStatistics = resultCollection.getStatistics();
       System.out.printf(
           "A batch of documents statistics, transaction count: %s, valid document count: %s.%n",
           batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());
      
       // Extracted key phrase for each of documents from a batch of documents
       resultCollection.forEach(extractKeyPhraseResult -> {
           System.out.printf("Document ID: %s%n", extractKeyPhraseResult.getId());
           // Valid document
           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 documents. If not set, uses "en" for English as default.
      options - The options to configure the scoring model for documents and show statistics.
      Returns:
      A ExtractKeyPhrasesResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
    • extractKeyPhrasesBatchWithResponse

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

      Code Sample

      Extracts key phrases with http response in a list of TextDocumentInput with request options.

       List<TextDocumentInput> textDocumentInputs = Arrays.asList(
           new TextDocumentInput("1", "My cat might need to see a veterinarian.").setLanguage("en"),
           new TextDocumentInput("2", "The pitot tube is used to measure airspeed.").setLanguage("en")
       );
      
       // Extracting batch key phrases
       Response<ExtractKeyPhrasesResultCollection> response =
           textAnalyticsClient.extractKeyPhrasesBatchWithResponse(textDocumentInputs,
               new TextAnalyticsRequestOptions().setIncludeStatistics(true), Context.NONE);
      
      
       // 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(
           "A batch of documents statistics, transaction count: %s, valid document count: %s.%n",
           batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());
      
       // Extracted key phrase for each of documents from a batch of documents
       resultCollection.forEach(extractKeyPhraseResult -> {
           System.out.printf("Document ID: %s%n", extractKeyPhraseResult.getId());
           // Valid document
           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.
      options - The options to configure the scoring model for documents and show statistics.
      context - Additional context that is passed through the Http pipeline during the service call.
      Returns:
      A Response that contains a ExtractKeyPhrasesResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
    • analyzeSentiment

      public 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 sentiments of documents

       final DocumentSentiment documentSentiment =
           textAnalyticsClient.analyzeSentiment("The hotel was dark and unclean.");
      
       System.out.printf(
           "Recognized sentiment: %s, positive score: %.2f, neutral score: %.2f, negative score: %.2f.%n",
           documentSentiment.getSentiment(),
           documentSentiment.getConfidenceScores().getPositive(),
           documentSentiment.getConfidenceScores().getNeutral(),
           documentSentiment.getConfidenceScores().getNegative());
      
       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 analyzed document sentiment of the document.
      Throws:
      NullPointerException - if document is null.
      TextAnalyticsException - if the response returned with an error.
    • analyzeSentiment

      public 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.

       final DocumentSentiment documentSentiment = textAnalyticsClient.analyzeSentiment(
           "The hotel was dark and unclean.", "en");
      
       System.out.printf(
           "Recognized sentiment: %s, positive score: %.2f, neutral score: %.2f, negative score: %.2f.%n",
           documentSentiment.getSentiment(),
           documentSentiment.getConfidenceScores().getPositive(),
           documentSentiment.getConfidenceScores().getNeutral(),
           documentSentiment.getConfidenceScores().getNegative());
      
       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 document. If not set, uses "en" for English as default.
      Returns:
      A analyzed document sentiment of the document.
      Throws:
      NullPointerException - if document is null.
      TextAnalyticsException - if the response returned with an error.
    • analyzeSentiment

      public 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.

       final DocumentSentiment documentSentiment = textAnalyticsClient.analyzeSentiment(
           "The hotel was dark and unclean.", "en",
           new AnalyzeSentimentOptions().setIncludeOpinionMining(true));
       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 document. If not set, uses "en" for English as default.
      options - The additional configurable options that may be passed when analyzing sentiments.
      Returns:
      A analyzed document sentiment of the document.
      Throws:
      NullPointerException - if document is null.
      TextAnalyticsException - if the response returned with an error.
    • analyzeSentimentBatch

      @Deprecated public AnalyzeSentimentResultCollection analyzeSentimentBatch(Iterable<String> documents, String language, TextAnalyticsRequestOptions 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.

      Code Sample

      Analyze the sentiments in a list of documents with a provided language representation and request options.

       List<String> documents = Arrays.asList(
           "The hotel was dark and unclean. The restaurant had amazing gnocchi.",
           "The restaurant had amazing gnocchi. The hotel was dark and unclean."
       );
      
       // Analyzing batch sentiments
       AnalyzeSentimentResultCollection resultCollection = textAnalyticsClient.analyzeSentimentBatch(
           documents, "en", new TextAnalyticsRequestOptions().setIncludeStatistics(true));
      
       // Batch statistics
       TextDocumentBatchStatistics batchStatistics = resultCollection.getStatistics();
       System.out.printf("A batch of documents statistics, transaction count: %s, valid document count: %s.%n",
           batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());
      
       // Analyzed sentiment for each of documents from a batch of documents
       resultCollection.forEach(analyzeSentimentResult -> {
           System.out.printf("Document ID: %s%n", analyzeSentimentResult.getId());
           // Valid document
           DocumentSentiment documentSentiment = analyzeSentimentResult.getDocumentSentiment();
           System.out.printf(
               "Recognized document sentiment: %s, positive score: %.2f, neutral score: %.2f,"
                   + " negative score: %.2f.%n",
               documentSentiment.getSentiment(),
               documentSentiment.getConfidenceScores().getPositive(),
               documentSentiment.getConfidenceScores().getNeutral(),
               documentSentiment.getConfidenceScores().getNegative());
           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 documents. If not set, uses "en" for English as default.
      options - The options to configure the scoring model for documents and show statistics.
      Returns:
      A AnalyzeSentimentResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
    • analyzeSentimentBatch

      public 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.

       List<String> documents = Arrays.asList(
           "The hotel was dark and unclean. The restaurant had amazing gnocchi.",
           "The restaurant had amazing gnocchi. The hotel was dark and unclean."
       );
      
       // Analyzing batch sentiments
       AnalyzeSentimentResultCollection resultCollection = textAnalyticsClient.analyzeSentimentBatch(
           documents, "en", new AnalyzeSentimentOptions().setIncludeOpinionMining(true));
      
       // Analyzed sentiment for each of documents from a batch of documents
       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("\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:
      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 sentiments.
      Returns:
      A AnalyzeSentimentResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
    • analyzeSentimentBatchWithResponse

      @Deprecated public Response<AnalyzeSentimentResultCollection> analyzeSentimentBatchWithResponse(Iterable<TextDocumentInput> documents, TextAnalyticsRequestOptions options, Context context)
      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 sentiment in a list of document with provided request options.

       List<TextDocumentInput> textDocumentInputs = Arrays.asList(
           new TextDocumentInput("1", "The hotel was dark and unclean. The restaurant had amazing gnocchi.")
               .setLanguage("en"),
           new TextDocumentInput("2", "The restaurant had amazing gnocchi. The hotel was dark and unclean.")
               .setLanguage("en")
       );
      
       // Analyzing batch sentiments
       Response<AnalyzeSentimentResultCollection> response =
           textAnalyticsClient.analyzeSentimentBatchWithResponse(textDocumentInputs,
               new TextAnalyticsRequestOptions().setIncludeStatistics(true), Context.NONE);
      
       // 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("A batch of documents statistics, transaction count: %s, valid document count: %s.%n",
           batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());
      
       // Analyzed sentiment for each of documents from a batch of documents
       resultCollection.forEach(analyzeSentimentResult -> {
           System.out.printf("Document ID: %s%n", analyzeSentimentResult.getId());
           // Valid document
           DocumentSentiment documentSentiment = analyzeSentimentResult.getDocumentSentiment();
           System.out.printf(
               "Recognized document sentiment: %s, positive score: %.2f, neutral score: %.2f, "
                   + "negative score: %.2f.%n",
               documentSentiment.getSentiment(),
               documentSentiment.getConfidenceScores().getPositive(),
               documentSentiment.getConfidenceScores().getNeutral(),
               documentSentiment.getConfidenceScores().getNegative());
           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.
      context - Additional context that is passed through the Http pipeline during the service call.
      Returns:
      A Response that contains a AnalyzeSentimentResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
    • analyzeSentimentBatchWithResponse

      public Response<AnalyzeSentimentResultCollection> analyzeSentimentBatchWithResponse(Iterable<TextDocumentInput> documents, AnalyzeSentimentOptions options, Context context)
      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.

       List<TextDocumentInput> textDocumentInputs = Arrays.asList(
           new TextDocumentInput("1", "The hotel was dark and unclean. The restaurant had amazing gnocchi.")
               .setLanguage("en"),
           new TextDocumentInput("2", "The restaurant had amazing gnocchi. The hotel was dark and unclean.")
               .setLanguage("en")
       );
      
       AnalyzeSentimentOptions options = new AnalyzeSentimentOptions().setIncludeOpinionMining(true)
           .setIncludeStatistics(true);
      
       // Analyzing batch sentiments
       Response<AnalyzeSentimentResultCollection> response =
           textAnalyticsClient.analyzeSentimentBatchWithResponse(textDocumentInputs, options, Context.NONE);
      
       // 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("A batch of documents statistics, transaction count: %s, valid document count: %s.%n",
           batchStatistics.getTransactionCount(), batchStatistics.getValidDocumentCount());
      
       // Analyzed sentiment for each of documents from a batch of documents
       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("\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:
      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.
      context - Additional context that is passed through the Http pipeline during the service call.
      Returns:
      A Response that contains a AnalyzeSentimentResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
    • beginAnalyzeHealthcareEntities

      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 SyncPoller that polls the analyze healthcare operation until it has completed, has failed, or has been cancelled. The completed operation returns a PagedIterable of AnalyzeHealthcareEntitiesResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
      TextAnalyticsException - If analyze operation fails.
    • beginAnalyzeHealthcareEntities

      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.

      Code Sample

      Analyze healthcare entities, entity data sources, and entity relations in a list of document and provided request options to show statistics.

       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."));
       }
      
       // Request options: show statistics and model version
       AnalyzeHealthcareEntitiesOptions options = new AnalyzeHealthcareEntitiesOptions()
           .setIncludeStatistics(true);
      
       SyncPoller<AnalyzeHealthcareEntitiesOperationDetail, AnalyzeHealthcareEntitiesPagedIterable>
           syncPoller = textAnalyticsClient.beginAnalyzeHealthcareEntities(documents, options, Context.NONE);
      
       syncPoller.waitForCompletion();
       AnalyzeHealthcareEntitiesPagedIterable result = syncPoller.getFinalResult();
      
       // Task operation statistics
       final AnalyzeHealthcareEntitiesOperationDetail operationResult = syncPoller.poll().getValue();
       System.out.printf("Operation created time: %s, expiration time: %s.%n",
           operationResult.getCreatedAt(), operationResult.getExpiresAt());
      
       result.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.
      context - Additional context that is passed through the Http pipeline during the service call.
      Returns:
      A SyncPoller that polls the analyze healthcare operation until it has completed, has failed, or has been cancelled. The completed operation returns a PagedIterable of AnalyzeHealthcareEntitiesResultCollection.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
      TextAnalyticsException - If analyze operation fails.
    • beginAnalyzeActions

      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.",
           "My SSN is 859-98-0987"
       );
      
       SyncPoller<AnalyzeActionsOperationDetail, AnalyzeActionsResultPagedIterable> syncPoller =
           textAnalyticsClient.beginAnalyzeActions(
               documents,
               new TextAnalyticsActions().setDisplayName("{tasks_display_name}")
                   .setRecognizeEntitiesActions(new RecognizeEntitiesAction())
                   .setExtractKeyPhrasesActions(new ExtractKeyPhrasesAction()),
               "en",
               new AnalyzeActionsOptions().setIncludeStatistics(false));
       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.
      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 SyncPoller that polls the analyze a collection of actions operation until it has completed, has failed, or has been cancelled. The completed operation returns a AnalyzeActionsResultPagedIterable.
      Throws:
      NullPointerException - if documents is null.
      IllegalArgumentException - if documents is empty.
      TextAnalyticsException - If analyze operation fails.
    • beginAnalyzeActions

      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")
       );
      
       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.
      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 actions.
      context - Additional context that is passed through the Http pipeline during the service call.
      Returns:
      A SyncPoller that polls the analyze a collection of actions operation until it has completed, has failed, or has been cancelled. The completed operation returns a AnalyzeActionsResultPagedIterable.
      Throws:
      NullPointerException - if documents or actions is null.
      IllegalArgumentException - if documents is empty.
      TextAnalyticsException - If analyze operation fails.