The Text Analytics API is a suite of text analytics web services built with best-in-class Microsoft machine learning algorithms. The API can be used to analyze unstructured text for tasks such as sentiment analysis, key phrase extraction, and language detection. No training data is needed to use this API - just bring your text data. This API uses advanced natural language processing techniques to deliver best in class predictions.
Further documentation can be found in https://docs.microsoft.com/azure/cognitive-services/text-analytics/overview
The input document to be analyzed for detecting language.
The input document to be analyzed by the service.
DetectedLanguage contains the predicted language found in text, its confidence score, and its ISO 639-1 representation.
RecognizeEntitiesResult is a result object which contains the recognized entities from a particular document.
DetectLanguageResult is a result object which contains the detected language of a particular document.
CategorizedEntity contains information about a particular entity found in text.
New in version v3.1-preview: The offset property.
TextAnalyticsError contains the error code, message, and other details that explain why the batch or individual document failed to be processed by the service.
TextAnalyticsWarning contains the warning code and message that explains why the response has a warning.
ExtractKeyPhrasesResult is a result object which contains the key phrases found in a particular document.
RecognizeLinkedEntitiesResult is a result object which contains links to a well-known knowledge base, like for example, Wikipedia or Bing.
AnalyzeSentimentResult is a result object which contains the overall predicted sentiment and confidence scores for your document and a per-sentence sentiment prediction with scores.
TextDocumentStatistics contains information about the document payload.
DocumentError is an error object which represents an error on the individual document.
LinkedEntity contains a link to the well-known recognized entity in text. The link comes from a data source like Wikipedia or Bing. It additionally includes all of the matches of this entity found in the document.
New in version v3.1-preview: The bing_entity_search_api_id property.
A match for the linked entity found in text. Provides the confidence score of the prediction and where the entity was found in the text.
New in version v3.1-preview: The offset property.
TextDocumentBatchStatistics contains information about the request payload. Note: This object is not returned in the response and needs to be retrieved by a response hook.
SentenceSentiment contains the predicted sentiment and confidence scores for each individual sentence in the document.
New in version v3.1-preview: The offset and mined_opinions properties.
The confidence scores (Softmax scores) between 0 and 1. Higher values indicate higher confidence.
A mined opinion object represents an opinion we've extracted from a sentence. It consists of both a target that these opinions are about, and the assessments representing the opinion.
TargetSentiment contains the predicted sentiment, confidence scores and other information about a key component of a product/service. For example in "The food at Hotel Foo is good", "food" is an key component of "Hotel Foo".
AssessmentSentiment contains the predicted sentiment, confidence scores and other information about an assessment given about a particular target. For example, in the sentence "The food is good", the assessment of the target 'food' is 'good'.
RecognizePiiEntitiesResult is a result object which contains the recognized Personally Identifiable Information (PII) entities from a particular document.
New in version v3.1-preview: The redacted_text parameter.
PiiEntity contains information about a Personally Identifiable Information (PII) entity found in text.
AnalyzeHealthcareEntitiesResultItem contains the Healthcare entities from a particular document.
HealthcareEntity contains information about a Healthcare entity found in text.
HealthcareEntityDataSource contains information representing an entity reference in a known data source.
RecognizeEntitiesAction encapsulates the parameters for starting a long-running Entities Recognition operation.
If you just want to recognize entities in a list of documents, and not perform a batch of long running actions on the input of documents, call method recognize_entities instead of interfacing with this model.
RecognizeEntitiesAction encapsulates the parameters for starting a long-running Linked Entities Recognition operation.
If you just want to recognize linked entities in a list of documents, and not perform a batch of long running actions on the input of documents, call method recognize_linked_entities instead of interfacing with this model.
RecognizePiiEntitiesAction encapsulates the parameters for starting a long-running PII Entities Recognition operation.
If you just want to recognize pii entities in a list of documents, and not perform a batch of long running actions on the input of documents, call method recognize_pii_entities instead of interfacing with this model.
ExtractKeyPhrasesAction encapsulates the parameters for starting a long-running key phrase extraction operation
If you just want to extract key phrases from a list of documents, and not perform a batch of long running actions on the input of documents, call method extract_key_phrases instead of interfacing with this model.
AnalyzeBatchActionsResult contains the results of a recognize entities action on a list of documents. Returned by begin_analyze_batch_actions
AnalyzeBatchActionsError is an error object which represents an an error response for an action.
HealthcareRelation is a result object which represents a relation detected in a document.
Every HealthcareRelation is an entity graph of a certain relation type, where all entities are connected and have specific roles within the relation context.
A model representing a role in a relation.
For example, in "The subject took 100 mg of ibuprofen", "100 mg" is a dosage entity fulfilling the role "Dosage" in the extracted relation "DosageofMedication".
Contains various assertions about a HealthcareEntity.
For example, if an entity is a diagnosis, is this diagnosis 'conditional' on a symptom? Are the doctors 'certain' about this diagnosis? Is this diagnosis 'associated' with another diagnosis?
Text Analytics API versions supported by this package
The different domains of PII entities that users can filter by
The type of batch action that was applied to the documents
Type of roles entities can have in entity_relations. There may be roles not covered in this enum