Class TextAnalyticsClient
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 Summary
Modifier and TypeMethodDescriptionanalyzeSentiment
(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.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.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.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.analyzeSentimentBatch
(Iterable<String> documents, String language, TextAnalyticsRequestOptions options) Deprecated.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.analyzeSentimentBatchWithResponse
(Iterable<TextDocumentInput> documents, TextAnalyticsRequestOptions options, Context context) Deprecated.beginAnalyzeActions
(Iterable<TextDocumentInput> documents, TextAnalyticsActions actions, AnalyzeActionsOptions options, Context context) Execute actions, such as, entities recognition, PII entities recognition and key phrases extraction for a list ofdocuments
with provided request options.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 ofdocuments
with provided request options.beginAnalyzeHealthcareEntities
(Iterable<TextDocumentInput> documents, AnalyzeHealthcareEntitiesOptions options, Context context) Analyze healthcare entities, entity data sources, and entity relations in a list ofdocuments
with provided request options.beginAnalyzeHealthcareEntities
(Iterable<String> documents, String language, AnalyzeHealthcareEntitiesOptions options) Analyze healthcare entities, entity data sources, and entity relations in a list ofdocuments
with provided request options.detectLanguage
(String document) Returns the detected language and a confidence score between zero and one.detectLanguage
(String document, String countryHint) Returns the detected language and a confidence score between zero and one.detectLanguageBatch
(Iterable<String> documents, String countryHint, TextAnalyticsRequestOptions options) Detects Language for a batch of document with the provided country hint and request options.detectLanguageBatchWithResponse
(Iterable<DetectLanguageInput> documents, TextAnalyticsRequestOptions options, Context context) Detects Language for a batch ofdocument
with provided request options.extractKeyPhrases
(String document) Returns a list of strings denoting the key phrases in the document.extractKeyPhrases
(String document, String language) Returns a list of strings denoting the key phrases in the document.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.extractKeyPhrasesBatchWithResponse
(Iterable<TextDocumentInput> documents, TextAnalyticsRequestOptions options, Context context) Returns a list of strings denoting the key phrases in the a batch ofdocument
with request options.Gets default country hint code.Gets default language when the builder is setup.recognizeEntities
(String document) Returns a list of general categorized entities in the provided document.recognizeEntities
(String document, String language) Returns a list of general categorized entities in the provided document with provided language code.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.recognizeEntitiesBatchWithResponse
(Iterable<TextDocumentInput> documents, TextAnalyticsRequestOptions options, Context context) Returns a list of general categorized entities for the provided list ofdocument
with provided request options.recognizeLinkedEntities
(String document) Returns a list of recognized entities with links to a well-known knowledge base for the provided document.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.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.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 ofdocument
and request options.recognizePiiEntities
(String document) Returns a list of Personally Identifiable Information(PII) entities in the provided document.recognizePiiEntities
(String document, String language) Returns a list of Personally Identifiable Information(PII) entities in the provided document with provided language code.recognizePiiEntities
(String document, String language, RecognizePiiEntitiesOptions options) Returns a list of Personally Identifiable Information(PII) entities in the provided document with provided language code.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.recognizePiiEntitiesBatchWithResponse
(Iterable<TextDocumentInput> documents, RecognizePiiEntitiesOptions options, Context context) Returns a list of Personally Identifiable Information(PII) entities for the provided list ofdocument
with provided request options.
-
Method Details
-
getDefaultCountryHint
Gets default country hint code.- Returns:
- The default country hint code
-
getDefaultLanguage
Gets default language when the builder is setup.- Returns:
- The default language
-
detectLanguage
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 inTextAnalyticsClientBuilder.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
- ifdocument
is null.
-
detectLanguage
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 stringcountryHint
= "" or "none".- Returns:
- The
detected language
of the document. - Throws:
NullPointerException
- ifdocument
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 stringcountryHint
= "" or "none".options
- Theoptions
to configure the scoring model for documents and show statistics.- Returns:
- A
DetectLanguageResultCollection
. - Throws:
NullPointerException
- ifdocuments
is null.IllegalArgumentException
- ifdocuments
is empty.
-
detectLanguageBatchWithResponse
public Response<DetectLanguageResultCollection> detectLanguageBatchWithResponse(Iterable<DetectLanguageInput> documents, TextAnalyticsRequestOptions options, Context context) Detects Language for a batch ofdocument
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 ofdocuments
to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.options
- Theoptions
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 aDetectLanguageResultCollection
. - Throws:
NullPointerException
- ifdocuments
is null.IllegalArgumentException
- ifdocuments
is empty.
-
recognizeEntities
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 methodTextAnalyticsClientBuilder.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 ofrecognized categorized entities
and warnings. - Throws:
NullPointerException
- ifdocument
is null.TextAnalyticsException
- if the response returned with anerror
.
-
recognizeEntities
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: thisCode 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 ofrecognized categorized entities
and warnings. - Throws:
NullPointerException
- ifdocument
is null.TextAnalyticsException
- if the response returned with anerror
.
-
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
- Theoptions
to configure the scoring model for documents and show statistics.- Returns:
- A
RecognizeEntitiesResultCollection
. - Throws:
NullPointerException
- ifdocuments
is null.IllegalArgumentException
- ifdocuments
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 ofdocument
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 ofdocuments
to recognize entities for. For text length limits, maximum batch size, and supported text encoding, see data limits.options
- Theoptions
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 aRecognizeEntitiesResultCollection
. - Throws:
NullPointerException
- ifdocuments
is null.IllegalArgumentException
- ifdocuments
is empty.
-
recognizePiiEntities
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 usingTextAnalyticsClientBuilder.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
- ifdocument
is null.TextAnalyticsException
- if the response returned with anerror
.
-
recognizePiiEntities
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: thisCode 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
- ifdocument
is null.TextAnalyticsException
- if the response returned with anerror
.
-
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: thisCode 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 configurableoptions
that may be passed when recognizing PII entities.- Returns:
- The
recognized PII entities collection
. - Throws:
NullPointerException
- ifdocument
is null.TextAnalyticsException
- if the response returned with anerror
.
-
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 configurableoptions
that may be passed when recognizing PII entities.- Returns:
- A
RecognizePiiEntitiesResultCollection
. - Throws:
NullPointerException
- ifdocuments
is null.IllegalArgumentException
- ifdocuments
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 ofdocument
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 ofdocuments
to recognize PII entities for. For text length limits, maximum batch size, and supported text encoding, see data limits.options
- The additional configurableoptions
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 aRecognizePiiEntitiesResultCollection
. - Throws:
NullPointerException
- ifdocuments
is null.IllegalArgumentException
- ifdocuments
is empty.
-
recognizeLinkedEntities
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 methodTextAnalyticsClientBuilder.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 ofrecognized linked entities
. - Throws:
NullPointerException
- ifdocument
is null.TextAnalyticsException
- if the response returned with anerror
.
-
recognizeLinkedEntities
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 ofrecognized linked entities
. - Throws:
NullPointerException
- ifdocument
is null.TextAnalyticsException
- if the response returned with anerror
.
-
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
- Theoptions
to configure the scoring model for documents and show statistics.- Returns:
- A
RecognizeLinkedEntitiesResultCollection
. - Throws:
NullPointerException
- ifdocuments
is null.IllegalArgumentException
- ifdocuments
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 ofdocument
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 ofdocuments
to recognize linked entities for. For text length limits, maximum batch size, and supported text encoding, see data limits.options
- Theoptions
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 aRecognizeLinkedEntitiesResultCollection
. - Throws:
NullPointerException
- ifdocuments
is null.IllegalArgumentException
- ifdocuments
is empty.
-
extractKeyPhrases
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 methodTextAnalyticsClientBuilder.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
- ifdocument
is null.TextAnalyticsException
- if the response returned with anerror
.
-
extractKeyPhrases
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
- ifdocument
is null.TextAnalyticsException
- if the response returned with anerror
.
-
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
- Theoptions
to configure the scoring model for documents and show statistics.- Returns:
- A
ExtractKeyPhrasesResultCollection
. - Throws:
NullPointerException
- ifdocuments
is null.IllegalArgumentException
- ifdocuments
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 ofdocument
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 ofdocuments
to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.options
- Theoptions
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 aExtractKeyPhrasesResultCollection
. - Throws:
NullPointerException
- ifdocuments
is null.IllegalArgumentException
- ifdocuments
is empty.
-
analyzeSentiment
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 methodTextAnalyticsClientBuilder.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
- ifdocument
is null.TextAnalyticsException
- if the response returned with anerror
.
-
analyzeSentiment
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
- ifdocument
is null.TextAnalyticsException
- if the response returned with anerror
.
-
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 theincludeOpinionMining
ofAnalyzeSentimentOptions
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 configurableoptions
that may be passed when analyzing sentiments.- Returns:
- A
analyzed document sentiment
of the document. - Throws:
NullPointerException
- ifdocument
is null.TextAnalyticsException
- if the response returned with anerror
.
-
analyzeSentimentBatch
@Deprecated public AnalyzeSentimentResultCollection analyzeSentimentBatch(Iterable<String> documents, String language, TextAnalyticsRequestOptions options) Deprecated.Please use theanalyzeSentimentBatch(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.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
- Theoptions
to configure the scoring model for documents and show statistics.- Returns:
- A
AnalyzeSentimentResultCollection
. - Throws:
NullPointerException
- ifdocuments
is null.IllegalArgumentException
- ifdocuments
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 theincludeOpinionMining
ofAnalyzeSentimentOptions
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 configurableoptions
that may be passed when analyzing sentiments.- Returns:
- A
AnalyzeSentimentResultCollection
. - Throws:
NullPointerException
- ifdocuments
is null.IllegalArgumentException
- ifdocuments
is empty.
-
analyzeSentimentBatchWithResponse
@Deprecated public Response<AnalyzeSentimentResultCollection> analyzeSentimentBatchWithResponse(Iterable<TextDocumentInput> documents, TextAnalyticsRequestOptions options, Context context) Deprecated.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 ofdocuments
to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.options
- Theoptions
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 aAnalyzeSentimentResultCollection
. - Throws:
NullPointerException
- ifdocuments
is null.IllegalArgumentException
- ifdocuments
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 theincludeOpinionMining
ofAnalyzeSentimentOptions
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 providedAnalyzeSentimentOptions
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 ofdocuments
to be analyzed. For text length limits, maximum batch size, and supported text encoding, see data limits.options
- The additional configurableoptions
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 aAnalyzeSentimentResultCollection
. - Throws:
NullPointerException
- ifdocuments
is null.IllegalArgumentException
- ifdocuments
is empty.
-
beginAnalyzeHealthcareEntities
public SyncPoller<AnalyzeHealthcareEntitiesOperationDetail,AnalyzeHealthcareEntitiesPagedIterable> beginAnalyzeHealthcareEntities(Iterable<String> documents, String language, AnalyzeHealthcareEntitiesOptions options) Analyze healthcare entities, entity data sources, and entity relations in a list ofdocuments
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 configurableoptions
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 aPagedIterable
ofAnalyzeHealthcareEntitiesResultCollection
. - Throws:
NullPointerException
- ifdocuments
is null.IllegalArgumentException
- ifdocuments
is empty.TextAnalyticsException
- If analyze operation fails.
-
beginAnalyzeHealthcareEntities
public SyncPoller<AnalyzeHealthcareEntitiesOperationDetail,AnalyzeHealthcareEntitiesPagedIterable> beginAnalyzeHealthcareEntities(Iterable<TextDocumentInput> documents, AnalyzeHealthcareEntitiesOptions options, Context context) Analyze healthcare entities, entity data sources, and entity relations in a list ofdocuments
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 ofdocuments
to be analyzed.options
- The additional configurableoptions
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 aPagedIterable
ofAnalyzeHealthcareEntitiesResultCollection
. - Throws:
NullPointerException
- ifdocuments
is null.IllegalArgumentException
- ifdocuments
is empty.TextAnalyticsException
- If analyze operation fails.
-
beginAnalyzeActions
public SyncPoller<AnalyzeActionsOperationDetail,AnalyzeActionsResultPagedIterable> 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 ofdocuments
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
- Theactions
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 configurableoptions
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 aAnalyzeActionsResultPagedIterable
. - Throws:
NullPointerException
- ifdocuments
is null.IllegalArgumentException
- ifdocuments
is empty.TextAnalyticsException
- If analyze operation fails.
-
beginAnalyzeActions
public SyncPoller<AnalyzeActionsOperationDetail,AnalyzeActionsResultPagedIterable> beginAnalyzeActions(Iterable<TextDocumentInput> documents, TextAnalyticsActions actions, AnalyzeActionsOptions options, Context context) Execute actions, such as, entities recognition, PII entities recognition and key phrases extraction for a list ofdocuments
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 ofdocuments
to be analyzed.actions
- Theactions
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 configurableoptions
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 aAnalyzeActionsResultPagedIterable
. - Throws:
NullPointerException
- ifdocuments
oractions
is null.IllegalArgumentException
- ifdocuments
is empty.TextAnalyticsException
- If analyze operation fails.
-
analyzeSentimentBatch(Iterable, String, AnalyzeSentimentOptions)
.