azure.ai.textanalytics package

class azure.ai.textanalytics.TextAnalyticsApiVersion

Text Analytics API versions supported by this package

V3_0 = 'v3.0'

V3_1_PREVIEW = 'v3.1-preview.5'

this is the default version

class azure.ai.textanalytics.TextAnalyticsClient(endpoint: str, credential: Union[AzureKeyCredential, TokenCredential], **kwargs: Any)

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

Parameters

• endpoint (str) – Supported Cognitive Services or Text Analytics resource endpoints (protocol and hostname, for example: https://westus2.api.cognitive.microsoft.com).

• credential (AzureKeyCredential or TokenCredential) – Credentials needed for the client to connect to Azure. This can be the an instance of AzureKeyCredential if using a cognitive services/text analytics API key or a token credential from azure.identity.

Keyword Arguments

• default_country_hint (str) – Sets the default country_hint to use for all operations. Defaults to “US”. If you don’t want to use a country hint, pass the string “none”.

• default_language (str) – Sets the default language to use for all operations. Defaults to “en”.

• api_version (str or TextAnalyticsApiVersion) – The API version of the service to use for requests. It defaults to the latest service version. Setting to an older version may result in reduced feature compatibility.

Example:

Creating the TextAnalyticsClient with endpoint and API key.

from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics import TextAnalyticsClient
endpoint = os.environ["AZURE_TEXT_ANALYTICS_ENDPOINT"]
key = os.environ["AZURE_TEXT_ANALYTICS_KEY"]

text_analytics_client = TextAnalyticsClient(endpoint, AzureKeyCredential(key))

Creating the TextAnalyticsClient with endpoint and token credential from Azure Active Directory.

from azure.ai.textanalytics import TextAnalyticsClient
from azure.identity import DefaultAzureCredential

endpoint = os.environ["AZURE_TEXT_ANALYTICS_ENDPOINT"]
credential = DefaultAzureCredential()

text_analytics_client = TextAnalyticsClient(endpoint, credential=credential)

analyze_sentiment(documents: Union[List[str], List[TextDocumentInput], List[Dict[str, str]]], **kwargs: Any) → List[Union[AnalyzeSentimentResult, DocumentError]]

Analyze sentiment for a batch of documents. Turn on opinion mining with show_opinion_mining.

Returns a sentiment prediction, as well as sentiment scores for each sentiment class (Positive, Negative, and Neutral) for the document and each sentence within it.

See https://docs.microsoft.com/azure/cognitive-services/text-analytics/overview#data-limits for document length limits, maximum batch size, and supported text encoding.

Parameters

documents (list[str] or list[TextDocumentInput] or list[dict[str, str]]) – The set of documents to process as part of this batch. If you wish to specify the ID and language on a per-item basis you must use as input a list[TextDocumentInput] or a list of dict representations of TextDocumentInput, like {“id”: “1”, “language”: “en”, “text”: “hello world”}.

Keyword Arguments

• show_opinion_mining (bool) – Whether to mine the opinions of a sentence and conduct more granular analysis around the aspects of a product or service (also known as aspect-based sentiment analysis). If set to true, the returned SentenceSentiment objects will have property mined_opinions containing the result of this analysis. Only available for API version v3.1-preview and up.

• language (str) – The 2 letter ISO 639-1 representation of language for the entire batch. For example, use “en” for English; “es” for Spanish etc. If not set, uses “en” for English as default. Per-document language will take precedence over whole batch language. See https://aka.ms/talangs for supported languages in Text Analytics API.

• model_version (str) – This value indicates which model will be used for scoring, e.g. “latest”, “2019-10-01”. If a model-version is not specified, the API will default to the latest, non-preview version. See here for more info: https://aka.ms/text-analytics-model-versioning

• show_stats (bool) – If set to true, response will contain document level statistics in the statistics field of the document-level response.

• string_index_type (str) – Specifies the method used to interpret string offsets. UnicodeCodePoint, the Python encoding, is the default. To override the Python default, you can also pass in Utf16CodePoint or TextElement_v8. For additional information see https://aka.ms/text-analytics-offsets

• disable_service_logs (bool) – If set to true, you opt-out of having your text input logged on the service side for troubleshooting. By default, Text Analytics logs your input text for 48 hours, solely to allow for troubleshooting issues in providing you with the Text Analytics natural language processing functions. Setting this parameter to true, disables input logging and may limit our ability to remediate issues that occur. Please see Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for additional details, and Microsoft Responsible AI principles at https://www.microsoft.com/ai/responsible-ai.

New in version v3.1-preview: The show_opinion_mining parameter. The string_index_type parameter.

Returns

The combined list of AnalyzeSentimentResult and DocumentError in the order the original documents were passed in.

Return type

list[AnalyzeSentimentResult, DocumentError]

Raises

HttpResponseError or TypeError or ValueError –

Example:

Analyze sentiment in a batch of documents.

from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics import TextAnalyticsClient

endpoint = os.environ["AZURE_TEXT_ANALYTICS_ENDPOINT"]
key = os.environ["AZURE_TEXT_ANALYTICS_KEY"]

text_analytics_client = TextAnalyticsClient(endpoint=endpoint, credential=AzureKeyCredential(key))

documents = [
    """I had the best day of my life. I decided to go sky-diving and it made me appreciate my whole life so much more.
    I developed a deep-connection with my instructor as well, and I feel as if I've made a life-long friend in her.""",
    """This was a waste of my time. All of the views on this drop are extremely boring, all I saw was grass. 0/10 would
    not recommend to any divers, even first timers.""",
    """This was pretty good! The sights were ok, and I had fun with my instructors! Can't complain too much about my experience""",
    """I only have one word for my experience: WOW!!! I can't believe I have had such a wonderful skydiving company right
    in my backyard this whole time! I will definitely be a repeat customer, and I want to take my grandmother skydiving too,
    I know she'll love it!"""
]


result = text_analytics_client.analyze_sentiment(documents, show_opinion_mining=True)
docs = [doc for doc in result if not doc.is_error]

print("Let's visualize the sentiment of each of these documents")
for idx, doc in enumerate(docs):
    print("Document text: {}".format(documents[idx]))
    print("Overall sentiment: {}".format(doc.sentiment))

begin_analyze_actions(documents: Union[List[str], List[TextDocumentInput], List[Dict[str, str]]], actions: List[Union[RecognizeEntitiesAction, RecognizeLinkedEntitiesAction, RecognizePiiEntitiesAction, ExtractKeyPhrasesAction, AnalyzeSentimentAction]] # pylint: disable=line-too-long, **kwargs: Any) → LROPoller[ItemPaged[AnalyzeActionsResult]]

Start a long-running operation to perform a variety of text analysis actions over a batch of documents.

Parameters

• documents (list[str] or list[TextDocumentInput] or list[dict[str, str]]) – The set of documents to process as part of this batch. If you wish to specify the ID and language on a per-item basis you must use as input a list[TextDocumentInput] or a list of dict representations of TextDocumentInput, like {“id”: “1”, “language”: “en”, “text”: “hello world”}.

• actions (list[RecognizeEntitiesAction or RecognizePiiEntitiesAction or ExtractKeyPhrasesAction or RecognizeLinkedEntitiesAction or AnalyzeSentimentAction]) – A heterogeneous list of actions to perform on the inputted documents. Each action object encapsulates the parameters used for the particular action type. The outputted action results will be in the same order you inputted your actions. Duplicate actions in list not supported.

Keyword Arguments

• display_name (str) – An optional display name to set for the requested analysis.

• language (str) – The 2 letter ISO 639-1 representation of language for the entire batch. For example, use “en” for English; “es” for Spanish etc. If not set, uses “en” for English as default. Per-document language will take precedence over whole batch language. See https://aka.ms/talangs for supported languages in Text Analytics API.

• show_stats (bool) – If set to true, response will contain document level statistics.

• polling_interval (int) – Waiting time between two polls for LRO operations if no Retry-After header is present. Defaults to 30 seconds.

Returns

An instance of an LROPoller. Call result() on the poller object to return a pageable heterogeneous list of the action results in the order the actions were sent in this method.

Return type

LROPoller[ItemPaged[ AnalyzeActionsResult]]

Raises

HttpResponseError or TypeError or ValueError or NotImplementedError –

Example:

Start a long-running operation to perform a variety of text analysis actions over a batch of documents.

from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics import (
    TextAnalyticsClient,
    RecognizeEntitiesAction,
    RecognizeLinkedEntitiesAction,
    RecognizePiiEntitiesAction,
    ExtractKeyPhrasesAction,
    AnalyzeSentimentAction,
)

endpoint = os.environ["AZURE_TEXT_ANALYTICS_ENDPOINT"]
key = os.environ["AZURE_TEXT_ANALYTICS_KEY"]

text_analytics_client = TextAnalyticsClient(
    endpoint=endpoint,
    credential=AzureKeyCredential(key),
)

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

poller = text_analytics_client.begin_analyze_actions(
    documents,
    display_name="Sample Text Analysis",
    actions=[
        RecognizeEntitiesAction(),
        RecognizePiiEntitiesAction(),
        ExtractKeyPhrasesAction(),
        RecognizeLinkedEntitiesAction(),
        AnalyzeSentimentAction()
    ],
)

result = poller.result()
action_results = [action_result for action_result in list(result) if not action_result.is_error]

first_action_result = action_results[0]
print("Results of Entities Recognition action:")
docs = [doc for doc in first_action_result.document_results if not doc.is_error]

for idx, doc in enumerate(docs):
    print("\nDocument text: {}".format(documents[idx]))
    for entity in doc.entities:
        print("Entity: {}".format(entity.text))
        print("...Category: {}".format(entity.category))
        print("...Confidence Score: {}".format(entity.confidence_score))
        print("...Offset: {}".format(entity.offset))
        print("...Length: {}".format(entity.length))
    print("------------------------------------------")

second_action_result = action_results[1]
print("Results of PII Entities Recognition action:")
docs = [doc for doc in second_action_result.document_results if not doc.is_error]

for idx, doc in enumerate(docs):
    print("Document text: {}".format(documents[idx]))
    print("Document text with redactions: {}".format(doc.redacted_text))
    for entity in doc.entities:
        print("Entity: {}".format(entity.text))
        print("...Category: {}".format(entity.category))
        print("...Confidence Score: {}\n".format(entity.confidence_score))
        print("...Offset: {}".format(entity.offset))
        print("...Length: {}".format(entity.length))
    print("------------------------------------------")

third_action_result = action_results[2]
print("Results of Key Phrase Extraction action:")
docs = [doc for doc in third_action_result.document_results if not doc.is_error]

for idx, doc in enumerate(docs):
    print("Document text: {}\n".format(documents[idx]))
    print("Key Phrases: {}\n".format(doc.key_phrases))
    print("------------------------------------------")

fourth_action_result = action_results[3]
print("Results of Linked Entities Recognition action:")
docs = [doc for doc in fourth_action_result.document_results if not doc.is_error]

for idx, doc in enumerate(docs):
    print("Document text: {}\n".format(documents[idx]))
    for linked_entity in doc.entities:
        print("Entity name: {}".format(linked_entity.name))
        print("...Data source: {}".format(linked_entity.data_source))
        print("...Data source language: {}".format(linked_entity.language))
        print("...Data source entity ID: {}".format(linked_entity.data_source_entity_id))
        print("...Data source URL: {}".format(linked_entity.url))
        print("...Document matches:")
        for match in linked_entity.matches:
            print("......Match text: {}".format(match.text))
            print(".........Confidence Score: {}".format(match.confidence_score))
            print(".........Offset: {}".format(match.offset))
            print(".........Length: {}".format(match.length))
    print("------------------------------------------")

fifth_action_result = action_results[4]
print("Results of Sentiment Analysis action:")
docs = [doc for doc in fifth_action_result.document_results if not doc.is_error]

for doc in docs:
    print("Overall sentiment: {}".format(doc.sentiment))
    print("Scores: positive={}; neutral={}; negative={} \n".format(
        doc.confidence_scores.positive,
        doc.confidence_scores.neutral,
        doc.confidence_scores.negative,
    ))
    print("------------------------------------------")

begin_analyze_healthcare_entities(documents: Union[List[str], List[TextDocumentInput], List[Dict[str, str]]], **kwargs: Any) → LROPoller[ItemPaged[AnalyzeHealthcareEntitiesResultItem]]

Analyze healthcare entities and identify relationships between these entities in a batch of documents.

Entities are associated with references that can be found in existing knowledge bases, such as UMLS, CHV, MSH, etc.

We also extract the relations found between entities, for example in “The subject took 100 mg of ibuprofen”, we would extract the relationship between the “100 mg” dosage and the “ibuprofen” medication.

NOTE: this endpoint is currently in gated preview, meaning your subscription needs to be allow-listed for you to use this endpoint. More information about that here: https://aka.ms/text-analytics-health-request-access

Parameters

documents (list[str] or list[TextDocumentInput] or list[dict[str, str]]) – The set of documents to process as part of this batch. If you wish to specify the ID and language on a per-item basis you must use as input a list[TextDocumentInput] or a list of dict representations of TextDocumentInput, like {“id”: “1”, “language”: “en”, “text”: “hello world”}.

Keyword Arguments

• model_version (str) – This value indicates which model will be used for scoring, e.g. “latest”, “2019-10-01”. If a model-version is not specified, the API will default to the latest, non-preview version. Currently not working on the service side at time of release, as service will only use the latest model. Service is aware, and once it’s been fixed on the service side, the SDK should work automatically. See here for more info: https://aka.ms/text-analytics-model-versioning

• show_stats (bool) – If set to true, response will contain document level statistics.

• string_index_type (str) – Specifies the method used to interpret string offsets. UnicodeCodePoint, the Python encoding, is the default. To override the Python default, you can also pass in Utf16CodePoint or TextElement_v8. For additional information see https://aka.ms/text-analytics-offsets

• polling_interval (int) – Waiting time between two polls for LRO operations if no Retry-After header is present. Defaults to 5 seconds.

• continuation_token (str) – A continuation token to restart a poller from a saved state.

• disable_service_logs (bool) – If set to true, you opt-out of having your text input logged on the service side for troubleshooting. By default, Text Analytics logs your input text for 48 hours, solely to allow for troubleshooting issues in providing you with the Text Analytics natural language processing functions. Setting this parameter to true, disables input logging and may limit our ability to remediate issues that occur. Please see Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for additional details, and Microsoft Responsible AI principles at https://www.microsoft.com/ai/responsible-ai.

Returns

An instance of an AnalyzeHealthcareEntitiesLROPoller. Call result() on the this object to return a pageable of AnalyzeHealthcareEntitiesResultItem.

Return type

LROPoller[ItemPaged[ AnalyzeHealthcareEntitiesResultItem]]

Raises

HttpResponseError or TypeError or ValueError or NotImplementedError –

Example:

Recognize healthcare entities in a batch of documents.

import os
from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics import TextAnalyticsClient, HealthcareEntityRelationType, HealthcareEntityRelationRoleType

endpoint = os.environ["AZURE_TEXT_ANALYTICS_ENDPOINT"]
key = os.environ["AZURE_TEXT_ANALYTICS_KEY"]

text_analytics_client = TextAnalyticsClient(
    endpoint=endpoint,
    credential=AzureKeyCredential(key),
)

documents = [
    """
    Patient needs to take 100 mg of ibuprofen, and 3 mg of potassium. Also needs to take
    10 mg of Zocor.
    """,
    """
    Patient needs to take 50 mg of ibuprofen, and 2 mg of Coumadin.
    """
]

poller = text_analytics_client.begin_analyze_healthcare_entities(documents)
result = poller.result()

docs = [doc for doc in result if not doc.is_error]

print("Let's first visualize the outputted healthcare result:")
for idx, doc in enumerate(docs):
    for entity in doc.entities:
        print("Entity: {}".format(entity.text))
        print("...Normalized Text: {}".format(entity.normalized_text))
        print("...Category: {}".format(entity.category))
        print("...Subcategory: {}".format(entity.subcategory))
        print("...Offset: {}".format(entity.offset))
        print("...Confidence score: {}".format(entity.confidence_score))
        if entity.data_sources is not None:
            print("...Data Sources:")
            for data_source in entity.data_sources:
                print("......Entity ID: {}".format(data_source.entity_id))
                print("......Name: {}".format(data_source.name))
        if entity.assertion is not None:
            print("...Assertion:")
            print("......Conditionality: {}".format(entity.assertion.conditionality))
            print("......Certainty: {}".format(entity.assertion.certainty))
            print("......Association: {}".format(entity.assertion.association))
    for relation in doc.entity_relations:
        print("Relation of type: {} has the following roles".format(relation.relation_type))
        for role in relation.roles:
            print("...Role '{}' with entity '{}'".format(role.name, role.entity.text))
    print("------------------------------------------")

print("Now, let's get all of medication dosage relations from the documents")
dosage_of_medication_relations = [
    entity_relation
    for doc in docs
    for entity_relation in doc.entity_relations if entity_relation.relation_type == HealthcareEntityRelationType.DOSAGE_OF_MEDICATION
]

close() → None

Close sockets opened by the client. Calling this method is unnecessary when using the client as a context manager.

detect_language(documents: Union[List[str], List[DetectLanguageInput], List[Dict[str, str]]], **kwargs: Any) → List[Union[DetectLanguageResult, DocumentError]]

Detect language for a batch of documents.

Returns the detected language and a numeric score between zero and one. Scores close to one indicate 100% certainty that the identified language is true. See https://aka.ms/talangs for the list of enabled languages.

See https://docs.microsoft.com/azure/cognitive-services/text-analytics/overview#data-limits for document length limits, maximum batch size, and supported text encoding.

Parameters

documents (list[str] or list[DetectLanguageInput] or list[dict[str, str]]) – The set of documents to process as part of this batch. If you wish to specify the ID and country_hint on a per-item basis you must use as input a list[DetectLanguageInput] or a list of dict representations of DetectLanguageInput, like {“id”: “1”, “country_hint”: “us”, “text”: “hello world”}.

Keyword Arguments

• country_hint (str) – Country of origin hint for the entire batch. Accepts two letter country codes specified by ISO 3166-1 alpha-2. Per-document country hints will take precedence over whole batch hints. Defaults to “US”. If you don’t want to use a country hint, pass the string “none”.

• model_version (str) – Version of the model used on the service side for scoring, e.g. “latest”, “2019-10-01”. If a model version is not specified, the API will default to the latest, non-preview version. See here for more info: https://aka.ms/text-analytics-model-versioning

• show_stats (bool) – If set to true, response will contain document level statistics in the statistics field of the document-level response.

• disable_service_logs (bool) – If set to true, you opt-out of having your text input logged on the service side for troubleshooting. By default, Text Analytics logs your input text for 48 hours, solely to allow for troubleshooting issues in providing you with the Text Analytics natural language processing functions. Setting this parameter to true, disables input logging and may limit our ability to remediate issues that occur. Please see Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for additional details, and Microsoft Responsible AI principles at https://www.microsoft.com/ai/responsible-ai.

Returns

The combined list of DetectLanguageResult and DocumentError in the order the original documents were passed in.

Return type

list[DetectLanguageResult, DocumentError]

Raises

HttpResponseError or TypeError or ValueError –

Example:

Detecting language in a batch of documents.

from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics import TextAnalyticsClient

endpoint = os.environ["AZURE_TEXT_ANALYTICS_ENDPOINT"]
key = os.environ["AZURE_TEXT_ANALYTICS_KEY"]

text_analytics_client = TextAnalyticsClient(endpoint=endpoint, credential=AzureKeyCredential(key))
documents = [
    """
    The concierge Paulette was extremely helpful. Sadly when we arrived the elevator was broken, but with Paulette's help we barely noticed this inconvenience.
    She arranged for our baggage to be brought up to our room with no extra charge and gave us a free meal to refurbish all of the calories we lost from
    walking up the stairs :). Can't say enough good things about my experience!
    """,
    """
    最近由于工作压力太大，我们决定去富酒店度假。那儿的温泉实在太舒服了，我跟我丈夫都完全恢复了工作前的青春精神！加油！
    """
]

result = text_analytics_client.detect_language(documents)
reviewed_docs = [doc for doc in result if not doc.is_error]

print("Let's see what language each review is in!")

for idx, doc in enumerate(reviewed_docs):
    print("Review #{} is in '{}', which has ISO639-1 name '{}'\n".format(
        idx, doc.primary_language.name, doc.primary_language.iso6391_name
    ))
    if doc.is_error:
        print(doc.id, doc.error)

extract_key_phrases(documents: Union[List[str], List[TextDocumentInput], List[Dict[str, str]]], **kwargs: Any) → List[Union[ExtractKeyPhrasesResult, DocumentError]]

Extract key phrases from a batch of documents.

Returns a list of strings denoting the key phrases in the input text. For example, for the input text “The food was delicious and there were wonderful staff”, the API returns the main talking points: “food” and “wonderful staff”

See https://docs.microsoft.com/azure/cognitive-services/text-analytics/overview#data-limits for document length limits, maximum batch size, and supported text encoding.

Parameters

documents (list[str] or list[TextDocumentInput] or list[dict[str, str]]) – The set of documents to process as part of this batch. If you wish to specify the ID and language on a per-item basis you must use as input a list[TextDocumentInput] or a list of dict representations of TextDocumentInput, like {“id”: “1”, “language”: “en”, “text”: “hello world”}.

Keyword Arguments

• language (str) – The 2 letter ISO 639-1 representation of language for the entire batch. For example, use “en” for English; “es” for Spanish etc. If not set, uses “en” for English as default. Per-document language will take precedence over whole batch language. See https://aka.ms/talangs for supported languages in Text Analytics API.

• model_version (str) – This value indicates which model will be used for scoring, e.g. “latest”, “2019-10-01”. If a model-version is not specified, the API will default to the latest, non-preview version. See here for more info: https://aka.ms/text-analytics-model-versioning

• show_stats (bool) – If set to true, response will contain document level statistics in the statistics field of the document-level response.

• disable_service_logs (bool) – If set to true, you opt-out of having your text input logged on the service side for troubleshooting. By default, Text Analytics logs your input text for 48 hours, solely to allow for troubleshooting issues in providing you with the Text Analytics natural language processing functions. Setting this parameter to true, disables input logging and may limit our ability to remediate issues that occur. Please see Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for additional details, and Microsoft Responsible AI principles at https://www.microsoft.com/ai/responsible-ai.

Returns

The combined list of ExtractKeyPhrasesResult and DocumentError in the order the original documents were passed in.

Return type

list[ExtractKeyPhrasesResult, DocumentError]

Raises

HttpResponseError or TypeError or ValueError –

Example:

Extract the key phrases in a batch of documents.

from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics import TextAnalyticsClient

endpoint = os.environ["AZURE_TEXT_ANALYTICS_ENDPOINT"]
key = os.environ["AZURE_TEXT_ANALYTICS_KEY"]

text_analytics_client = TextAnalyticsClient(endpoint=endpoint, credential=AzureKeyCredential(key))
articles = [
    """
    Washington, D.C. Autumn in DC is a uniquely beautiful season. The leaves fall from the trees
    in a city chockful of forrests, leaving yellow leaves on the ground and a clearer view of the
    blue sky above...
    """,
    """
    Redmond, WA. In the past few days, Microsoft has decided to further postpone the start date of
    its United States workers, due to the pandemic that rages with no end in sight...
    """,
    """
    Redmond, WA. Employees at Microsoft can be excited about the new coffee shop that will open on campus
    once workers no longer have to work remotely...
    """
]

result = text_analytics_client.extract_key_phrases(articles)
for idx, doc in enumerate(result):
    if not doc.is_error:
        print("Key phrases in article #{}: {}".format(
            idx + 1,
            ", ".join(doc.key_phrases)
        ))

recognize_entities(documents: Union[List[str], List[TextDocumentInput], List[Dict[str, str]]], **kwargs: Any) → List[Union[RecognizeEntitiesResult, DocumentError]]

Recognize entities for a batch of documents.

Identifies and categorizes entities in your text as people, places, organizations, date/time, quantities, percentages, currencies, and more. For the list of supported entity types, check: https://aka.ms/taner

See https://docs.microsoft.com/azure/cognitive-services/text-analytics/overview#data-limits for document length limits, maximum batch size, and supported text encoding.

Parameters

documents (list[str] or list[TextDocumentInput] or list[dict[str, str]]) – The set of documents to process as part of this batch. If you wish to specify the ID and language on a per-item basis you must use as input a list[TextDocumentInput] or a list of dict representations of TextDocumentInput, like {“id”: “1”, “language”: “en”, “text”: “hello world”}.

Keyword Arguments

• language (str) – The 2 letter ISO 639-1 representation of language for the entire batch. For example, use “en” for English; “es” for Spanish etc. If not set, uses “en” for English as default. Per-document language will take precedence over whole batch language. See https://aka.ms/talangs for supported languages in Text Analytics API.

• model_version (str) – This value indicates which model will be used for scoring, e.g. “latest”, “2019-10-01”. If a model-version is not specified, the API will default to the latest, non-preview version. See here for more info: https://aka.ms/text-analytics-model-versioning

• show_stats (bool) – If set to true, response will contain document level statistics in the statistics field of the document-level response.

• string_index_type (str) – Specifies the method used to interpret string offsets. UnicodeCodePoint, the Python encoding, is the default. To override the Python default, you can also pass in Utf16CodePoint or TextElement_v8`. For additional information see https://aka.ms/text-analytics-offsets

• disable_service_logs (bool) – If set to true, you opt-out of having your text input logged on the service side for troubleshooting. By default, Text Analytics logs your input text for 48 hours, solely to allow for troubleshooting issues in providing you with the Text Analytics natural language processing functions. Setting this parameter to true, disables input logging and may limit our ability to remediate issues that occur. Please see Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for additional details, and Microsoft Responsible AI principles at https://www.microsoft.com/ai/responsible-ai.

Returns

The combined list of RecognizeEntitiesResult and DocumentError in the order the original documents were passed in.

Return type

list[RecognizeEntitiesResult, DocumentError]

Raises

HttpResponseError or TypeError or ValueError –

Example:

Recognize entities in a batch of documents.

from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics import TextAnalyticsClient

endpoint = os.environ["AZURE_TEXT_ANALYTICS_ENDPOINT"]
key = os.environ["AZURE_TEXT_ANALYTICS_KEY"]

text_analytics_client = TextAnalyticsClient(endpoint=endpoint, credential=AzureKeyCredential(key))
reviews = [
    """I work for Foo Company, and we hired Contoso for our annual founding ceremony. The food
    was amazing and we all can't say enough good words about the quality and the level of service.""",
    """We at the Foo Company re-hired Contoso after all of our past successes with the company.
    Though the food was still great, I feel there has been a quality drop since their last time
    catering for us. Is anyone else running into the same problem?""",
    """Bar Company is over the moon about the service we received from Contoso, the best sliders ever!!!!"""
]

result = text_analytics_client.recognize_entities(reviews)
result = [review for review in result if not review.is_error]

for idx, review in enumerate(result):
    for entity in review.entities:
        print("Entity '{}' has category '{}'".format(entity.text, entity.category))

recognize_linked_entities(documents: Union[List[str], List[TextDocumentInput], List[Dict[str, str]]], **kwargs: Any) → List[Union[RecognizeLinkedEntitiesResult, DocumentError]]

Recognize linked entities from a well-known knowledge base for a batch of documents.

Identifies and disambiguates the identity of each entity found in text (for example, determining whether an occurrence of the word Mars refers to the planet, or to the Roman god of war). Recognized entities are associated with URLs to a well-known knowledge base, like Wikipedia.

See https://docs.microsoft.com/azure/cognitive-services/text-analytics/overview#data-limits for document length limits, maximum batch size, and supported text encoding.

Parameters

documents (list[str] or list[TextDocumentInput] or list[dict[str, str]]) – The set of documents to process as part of this batch. If you wish to specify the ID and language on a per-item basis you must use as input a list[TextDocumentInput] or a list of dict representations of TextDocumentInput, like {“id”: “1”, “language”: “en”, “text”: “hello world”}.

Keyword Arguments

• language (str) – The 2 letter ISO 639-1 representation of language for the entire batch. For example, use “en” for English; “es” for Spanish etc. If not set, uses “en” for English as default. Per-document language will take precedence over whole batch language. See https://aka.ms/talangs for supported languages in Text Analytics API.

• model_version (str) – This value indicates which model will be used for scoring, e.g. “latest”, “2019-10-01”. If a model-version is not specified, the API will default to the latest, non-preview version. See here for more info: https://aka.ms/text-analytics-model-versioning

• show_stats (bool) – If set to true, response will contain document level statistics in the statistics field of the document-level response.

• string_index_type (str) – Specifies the method used to interpret string offsets. UnicodeCodePoint, the Python encoding, is the default. To override the Python default, you can also pass in Utf16CodePoint or TextElement_v8. For additional information see https://aka.ms/text-analytics-offsets

• disable_service_logs (bool) – If set to true, you opt-out of having your text input logged on the service side for troubleshooting. By default, Text Analytics logs your input text for 48 hours, solely to allow for troubleshooting issues in providing you with the Text Analytics natural language processing functions. Setting this parameter to true, disables input logging and may limit our ability to remediate issues that occur. Please see Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for additional details, and Microsoft Responsible AI principles at https://www.microsoft.com/ai/responsible-ai.

Returns

The combined list of RecognizeLinkedEntitiesResult and DocumentError in the order the original documents were passed in.

Return type

list[RecognizeLinkedEntitiesResult, DocumentError]

Raises

HttpResponseError or TypeError or ValueError –

Example:

Recognize linked entities in a batch of documents.

from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics import TextAnalyticsClient

endpoint = os.environ["AZURE_TEXT_ANALYTICS_ENDPOINT"]
key = os.environ["AZURE_TEXT_ANALYTICS_KEY"]

text_analytics_client = TextAnalyticsClient(endpoint=endpoint, credential=AzureKeyCredential(key))
documents = [
    """
    Microsoft was founded by Bill Gates with some friends he met at Harvard. One of his friends,
    Steve Ballmer, eventually became CEO after Bill Gates as well. Steve Ballmer eventually stepped
    down as CEO of Microsoft, and was succeeded by Satya Nadella.
    Microsoft originally moved its headquarters to Bellevue, Wahsington in Januaray 1979, but is now
    headquartered in Redmond.
    """
]

result = text_analytics_client.recognize_linked_entities(documents)
docs = [doc for doc in result if not doc.is_error]

print(
    "Let's map each entity to it's Wikipedia article. I also want to see how many times each "
    "entity is mentioned in a document\n\n"
)
entity_to_url = {}
for doc in docs:
    for entity in doc.entities:
        print("Entity '{}' has been mentioned '{}' time(s)".format(
            entity.name, len(entity.matches)
        ))
        if entity.data_source == "Wikipedia":
            entity_to_url[entity.name] = entity.url

recognize_pii_entities(documents: Union[List[str], List[TextDocumentInput], List[Dict[str, str]]], **kwargs: Any) → List[Union[RecognizePiiEntitiesResult, DocumentError]]

Recognize entities containing personal information for a batch of documents.

Returns a list of personal information entities (“SSN”, “Bank Account”, etc) in the document. For the list of supported entity types, check https://aka.ms/tanerpii

See https://docs.microsoft.com/azure/cognitive-services/text-analytics/overview#data-limits for document length limits, maximum batch size, and supported text encoding.

Parameters

documents (list[str] or list[TextDocumentInput] or list[dict[str, str]]) – The set of documents to process as part of this batch. If you wish to specify the ID and language on a per-item basis you must use as input a list[TextDocumentInput] or a list of dict representations of TextDocumentInput, like {“id”: “1”, “language”: “en”, “text”: “hello world”}.

Keyword Arguments

• language (str) – The 2 letter ISO 639-1 representation of language for the entire batch. For example, use “en” for English; “es” for Spanish etc. If not set, uses “en” for English as default. Per-document language will take precedence over whole batch language. See https://aka.ms/talangs for supported languages in Text Analytics API.

• model_version (str) – This value indicates which model will be used for scoring, e.g. “latest”, “2019-10-01”. If a model-version is not specified, the API will default to the latest, non-preview version. See here for more info: https://aka.ms/text-analytics-model-versioning

• show_stats (bool) – If set to true, response will contain document level statistics in the statistics field of the document-level response.

• domain_filter (str or PiiEntityDomainType) – Filters the response entities to ones only included in the specified domain. I.e., if set to ‘phi’, will only return entities in the Protected Healthcare Information domain. See https://aka.ms/tanerpii for more information.

• categories_filter (list[PiiEntityCategoryType]) – Instead of filtering over all PII entity categories, you can pass in a list of the specific PII entity categories you want to filter out. For example, if you only want to filter out U.S. social security numbers in a document, you can pass in [PiiEntityCategoryType.US_SOCIAL_SECURITY_NUMBER] for this kwarg.

• string_index_type (str) – Specifies the method used to interpret string offsets. UnicodeCodePoint, the Python encoding, is the default. To override the Python default, you can also pass in Utf16CodePoint or TextElement_v8. For additional information see https://aka.ms/text-analytics-offsets

• disable_service_logs (bool) – If set to true, you opt-out of having your text input logged on the service side for troubleshooting. By default, Text Analytics logs your input text for 48 hours, solely to allow for troubleshooting issues in providing you with the Text Analytics natural language processing functions. Setting this parameter to true, disables input logging and may limit our ability to remediate issues that occur. Please see Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for additional details, and Microsoft Responsible AI principles at https://www.microsoft.com/ai/responsible-ai.

Returns

The combined list of RecognizePiiEntitiesResult and DocumentError in the order the original documents were passed in.

Return type

list[RecognizePiiEntitiesResult, DocumentError]

Raises

HttpResponseError or TypeError or ValueError –

Example:

Recognize personally identifiable information entities in a batch of documents.

from azure.core.credentials import AzureKeyCredential
from azure.ai.textanalytics import TextAnalyticsClient

endpoint = os.environ["AZURE_TEXT_ANALYTICS_ENDPOINT"]
key = os.environ["AZURE_TEXT_ANALYTICS_KEY"]

text_analytics_client = TextAnalyticsClient(
    endpoint=endpoint, credential=AzureKeyCredential(key)
)
documents = [
    """Parker Doe has repaid all of their loans as of 2020-04-25.
    Their SSN is 859-98-0987. To contact them, use their phone number
    555-555-5555. They are originally from Brazil and have Brazilian CPF number 998.214.865-68"""
]

result = text_analytics_client.recognize_pii_entities(documents)
docs = [doc for doc in result if not doc.is_error]

print(
    "Let's compare the original document with the documents after redaction. "
    "I also want to comb through all of the entities that got redacted"
)
for idx, doc in enumerate(docs):
    print("Document text: {}".format(documents[idx]))
    print("Redacted document text: {}".format(doc.redacted_text))
    for entity in doc.entities:
        print("...Entity '{}' with category '{}' got redacted".format(
            entity.text, entity.category
        ))

class azure.ai.textanalytics.DetectLanguageInput(**kwargs)

The input document to be analyzed for detecting language.

Keyword Arguments

• id (str) – Unique, non-empty document identifier.

• text (str) – The input text to process.

• country_hint (str) – A country hint to help better detect the language of the text. Accepts two letter country codes specified by ISO 3166-1 alpha-2. Defaults to “US”. Pass in the string “none” to not use a country_hint.

Variables

• id (str) – Required. Unique, non-empty document identifier.

• text (str) – Required. The input text to process.

• country_hint (str) – A country hint to help better detect the language of the text. Accepts two letter country codes specified by ISO 3166-1 alpha-2. Defaults to “US”. Pass in the string “none” to not use a country_hint.

as_dict(keep_readonly=True, key_transformer=<function attribute_transformer>, **kwargs)

Return a dict that can be JSONify using json.dump.

Advanced usage might optionaly use a callback as parameter:

Key is the attribute name used in Python. Attr_desc is a dict of metadata. Currently contains ‘type’ with the msrest type and ‘key’ with the RestAPI encoded key. Value is the current value in this object.

The string returned will be used to serialize the key. If the return type is a list, this is considered hierarchical result dict.

See the three examples in this file:

• attribute_transformer

• full_restapi_key_transformer

• last_restapi_key_transformer

If you want XML serialization, you can pass the kwargs is_xml=True.

Parameters

key_transformer (function) – A key transformer function.

Returns

A dict JSON compatible object

Return type

dict

classmethod deserialize(data, content_type=None)

Parse a str using the RestAPI syntax and return a model.

Parameters

• data (str) – A str using RestAPI structure. JSON by default.

• content_type (str) – JSON by default, set application/xml if XML.

Returns

An instance of this model

Raises

DeserializationError if something went wrong

classmethod enable_additional_properties_sending()

classmethod from_dict(data, key_extractors=None, content_type=None)

Parse a dict using given key extractor return a model.

By default consider key extractors (rest_key_case_insensitive_extractor, attribute_key_case_insensitive_extractor and last_rest_key_case_insensitive_extractor)

Parameters

• data (dict) – A dict using RestAPI structure

• content_type (str) – JSON by default, set application/xml if XML.

Returns

An instance of this model

Raises

DeserializationError if something went wrong

classmethod is_xml_model()

serialize(keep_readonly=False, **kwargs)

Return the JSON that would be sent to azure from this model.

This is an alias to as_dict(full_restapi_key_transformer, keep_readonly=False).

If you want XML serialization, you can pass the kwargs is_xml=True.

Parameters

keep_readonly (bool) – If you want to serialize the readonly attributes

Returns

A dict JSON compatible object

Return type

dict

validate()

Validate this model recursively and return a list of ValidationError.

Returns

A list of validation error

Return type

list

class azure.ai.textanalytics.TextDocumentInput(**kwargs)

The input document to be analyzed by the service.

Keyword Arguments

• id (str) – Unique, non-empty document identifier.

• text (str) – The input text to process.

• language (str) – This is the 2 letter ISO 639-1 representation of a language. For example, use “en” for English; “es” for Spanish etc. If not set, uses “en” for English as default.

Variables

• id (str) – Required. Unique, non-empty document identifier.

• text (str) – Required. The input text to process.

• language (str) – This is the 2 letter ISO 639-1 representation of a language. For example, use “en” for English; “es” for Spanish etc. If not set, uses “en” for English as default.

as_dict(keep_readonly=True, key_transformer=<function attribute_transformer>, **kwargs)

Return a dict that can be JSONify using json.dump.

Advanced usage might optionaly use a callback as parameter:

Key is the attribute name used in Python. Attr_desc is a dict of metadata. Currently contains ‘type’ with the msrest type and ‘key’ with the RestAPI encoded key. Value is the current value in this object.

The string returned will be used to serialize the key. If the return type is a list, this is considered hierarchical result dict.

See the three examples in this file:

• attribute_transformer

• full_restapi_key_transformer

• last_restapi_key_transformer

If you want XML serialization, you can pass the kwargs is_xml=True.

Parameters

key_transformer (function) – A key transformer function.

Returns

A dict JSON compatible object

Return type

dict

classmethod deserialize(data, content_type=None)

Parse a str using the RestAPI syntax and return a model.

Parameters

• data (str) – A str using RestAPI structure. JSON by default.

• content_type (str) – JSON by default, set application/xml if XML.

Returns

An instance of this model

Raises

DeserializationError if something went wrong

classmethod enable_additional_properties_sending()

classmethod from_dict(data, key_extractors=None, content_type=None)

Parse a dict using given key extractor return a model.

By default consider key extractors (rest_key_case_insensitive_extractor, attribute_key_case_insensitive_extractor and last_rest_key_case_insensitive_extractor)

Parameters

• data (dict) – A dict using RestAPI structure

• content_type (str) – JSON by default, set application/xml if XML.

Returns

An instance of this model

Raises

DeserializationError if something went wrong

get(key, default=None)

has_key(k)

classmethod is_xml_model()

items()

keys()

serialize(keep_readonly=False, **kwargs)

Return the JSON that would be sent to azure from this model.

This is an alias to as_dict(full_restapi_key_transformer, keep_readonly=False).

If you want XML serialization, you can pass the kwargs is_xml=True.

Parameters

keep_readonly (bool) – If you want to serialize the readonly attributes

Returns

A dict JSON compatible object

Return type

dict

update(*args, **kwargs)

validate()

Validate this model recursively and return a list of ValidationError.

Returns

A list of validation error

Return type

list

values()

class azure.ai.textanalytics.DetectedLanguage(**kwargs)

DetectedLanguage contains the predicted language found in text, its confidence score, and its ISO 639-1 representation.

Variables

• name (str) – Long name of a detected language (e.g. English, French).

• iso6391_name (str) – A two letter representation of the detected language according to the ISO 639-1 standard (e.g. en, fr).

• confidence_score (float) – A confidence score between 0 and 1. Scores close to 1 indicate 100% certainty that the identified language is true.

get(key, default=None)

has_key(k)

items()

keys()

update(*args, **kwargs)

values()

class azure.ai.textanalytics.RecognizeEntitiesResult(**kwargs)

RecognizeEntitiesResult is a result object which contains the recognized entities from a particular document.

Variables

• id (str) – Unique, non-empty document identifier that matches the document id that was passed in with the request. If not specified in the request, an id is assigned for the document.

• entities (list[CategorizedEntity]) – Recognized entities in the document.

• warnings (list[TextAnalyticsWarning]) – Warnings encountered while processing document. Results will still be returned if there are warnings, but they may not be fully accurate.

• statistics (TextDocumentStatistics) – If show_stats=True was specified in the request this field will contain information about the document payload.

• is_error (bool) – Boolean check for error item when iterating over list of results. Always False for an instance of a RecognizeEntitiesResult.

get(key, default=None)

has_key(k)

items()

keys()

update(*args, **kwargs)

values()

class azure.ai.textanalytics.DetectLanguageResult(**kwargs)

DetectLanguageResult is a result object which contains the detected language of a particular document.

Variables

• id (str) – Unique, non-empty document identifier that matches the document id that was passed in with the request. If not specified in the request, an id is assigned for the document.

• primary_language (DetectedLanguage) – The primary language detected in the document.

• warnings (list[TextAnalyticsWarning]) – Warnings encountered while processing document. Results will still be returned if there are warnings, but they may not be fully accurate.

• statistics (TextDocumentStatistics) – If show_stats=True was specified in the request this field will contain information about the document payload.

• is_error (bool) – Boolean check for error item when iterating over list of results. Always False for an instance of a DetectLanguageResult.

get(key, default=None)

has_key(k)

items()

keys()

update(*args, **kwargs)

values()

class azure.ai.textanalytics.CategorizedEntity(**kwargs)

CategorizedEntity contains information about a particular entity found in text.

Variables

• text (str) – Entity text as appears in the request.

• category (str) – Entity category, such as Person/Location/Org/SSN etc

• subcategory (str) – Entity subcategory, such as Age/Year/TimeRange etc

• length (int) – The entity text length. This value depends on the value of the string_index_type parameter set in the original request, which is UnicodeCodePoints by default. Only returned for API versions v3.1-preview and up.

• offset (int) – The entity text offset from the start of the document. The value depends on the value of the string_index_type parameter set in the original request, which is UnicodeCodePoints by default. Only returned for API versions v3.1-preview and up.

• confidence_score (float) – Confidence score between 0 and 1 of the extracted entity.

New in version v3.1-preview: The offset property.

get(key, default=None)

has_key(k)

items()

keys()

update(*args, **kwargs)

values()

class azure.ai.textanalytics.TextAnalyticsError(**kwargs)

TextAnalyticsError contains the error code, message, and other details that explain why the batch or individual document failed to be processed by the service.

Variables

• code (str) – Error code. Possible values include: ‘invalidRequest’, ‘invalidArgument’, ‘internalServerError’, ‘serviceUnavailable’, ‘invalidParameterValue’, ‘invalidRequestBodyFormat’, ‘emptyRequest’, ‘missingInputRecords’, ‘invalidDocument’, ‘modelVersionIncorrect’, ‘invalidDocumentBatch’, ‘unsupportedLanguageCode’, ‘invalidCountryHint’

• message (str) – Error message.

• target (str) – Error target.

get(key, default=None)

has_key(k)

items()

keys()

update(*args, **kwargs)

values()

class azure.ai.textanalytics.TextAnalyticsWarning(**kwargs)

TextAnalyticsWarning contains the warning code and message that explains why the response has a warning.

Variables

• code (str) – Warning code. Possible values include: ‘LongWordsInDocument’, ‘DocumentTruncated’.

• message (str) – Warning message.

get(key, default=None)

has_key(k)

items()

keys()

update(*args, **kwargs)

values()

class azure.ai.textanalytics.ExtractKeyPhrasesResult(**kwargs)

ExtractKeyPhrasesResult is a result object which contains the key phrases found in a particular document.

Variables

• id (str) – Unique, non-empty document identifier that matches the document id that was passed in with the request. If not specified in the request, an id is assigned for the document.

• key_phrases (list[str]) – A list of representative words or phrases. The number of key phrases returned is proportional to the number of words in the input document.

• warnings (list[TextAnalyticsWarning]) – Warnings encountered while processing document. Results will still be returned if there are warnings, but they may not be fully accurate.

• statistics (TextDocumentStatistics) – If show_stats=True was specified in the request this field will contain information about the document payload.

• is_error (bool) – Boolean check for error item when iterating over list of results. Always False for an instance of a ExtractKeyPhrasesResult.

get(key, default=None)

has_key(k)

items()

keys()

update(*args, **kwargs)

values()

class azure.ai.textanalytics.RecognizeLinkedEntitiesResult(**kwargs)

RecognizeLinkedEntitiesResult is a result object which contains links to a well-known knowledge base, like for example, Wikipedia or Bing.

Variables

• id (str) – Unique, non-empty document identifier that matches the document id that was passed in with the request. If not specified in the request, an id is assigned for the document.

• entities (list[LinkedEntity]) – Recognized well-known entities in the document.

• warnings (list[TextAnalyticsWarning]) – Warnings encountered while processing document. Results will still be returned if there are warnings, but they may not be fully accurate.

• statistics (TextDocumentStatistics) – If show_stats=True was specified in the request this field will contain information about the document payload.

• is_error (bool) – Boolean check for error item when iterating over list of results. Always False for an instance of a RecognizeLinkedEntitiesResult.

get(key, default=None)

has_key(k)

items()

keys()

update(*args, **kwargs)

values()

class azure.ai.textanalytics.AnalyzeSentimentResult(**kwargs)

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.

Variables

• id (str) – Unique, non-empty document identifier that matches the document id that was passed in with the request. If not specified in the request, an id is assigned for the document.

• sentiment (str) – Predicted sentiment for document (Negative, Neutral, Positive, or Mixed). Possible values include: ‘positive’, ‘neutral’, ‘negative’, ‘mixed’

• warnings (list[TextAnalyticsWarning]) – Warnings encountered while processing document. Results will still be returned if there are warnings, but they may not be fully accurate.

• statistics (TextDocumentStatistics) – If show_stats=True was specified in the request this field will contain information about the document payload.

• confidence_scores (SentimentConfidenceScores) – Document level sentiment confidence scores between 0 and 1 for each sentiment label.

• sentences (list[SentenceSentiment]) – Sentence level sentiment analysis.

• is_error (bool) – Boolean check for error item when iterating over list of results. Always False for an instance of a AnalyzeSentimentResult.

get(key, default=None)

has_key(k)

items()

keys()

update(*args, **kwargs)

values()

class azure.ai.textanalytics.TextDocumentStatistics(**kwargs)

TextDocumentStatistics contains information about the document payload.

Variables

• character_count (int) – Number of text elements recognized in the document.

• transaction_count (int) – Number of transactions for the document.

get(key, default=None)

has_key(k)

items()

keys()

update(*args, **kwargs)

values()

class azure.ai.textanalytics.DocumentError(**kwargs)

DocumentError is an error object which represents an error on the individual document.

Variables

• id (str) – Unique, non-empty document identifier that matches the document id that was passed in with the request. If not specified in the request, an id is assigned for the document.

• error (TextAnalyticsError) – The document error.

• is_error (bool) – Boolean check for error item when iterating over list of results. Always True for an instance of a DocumentError.

get(key, default=None)

has_key(k)

items()

keys()

update(*args, **kwargs)

values()

class azure.ai.textanalytics.LinkedEntity(**kwargs)

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.

Variables

• name (str) – Entity Linking formal name.

• matches (list[LinkedEntityMatch]) – List of instances this entity appears in the text.

• language (str) – Language used in the data source.

• data_source_entity_id (str) – Unique identifier of the recognized entity from the data source.

• url (str) – URL to the entity’s page from the data source.

• data_source (str) – Data source used to extract entity linking, such as Wiki/Bing etc.

• bing_entity_search_api_id (str) – Bing Entity Search unique identifier of the recognized entity. Use in conjunction with the Bing Entity Search SDK to fetch additional relevant information. Only available for API version v3.1-preview and up.

New in version v3.1-preview: The bing_entity_search_api_id property.

get(key, default=None)

has_key(k)

items()

keys()

update(*args, **kwargs)

values()

class azure.ai.textanalytics.LinkedEntityMatch(**kwargs)

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.

Variables

• confidence_score (float) – If a well-known item is recognized, a decimal number denoting the confidence level between 0 and 1 will be returned.

• text (str) – Entity text as appears in the request.

• length (int) – The linked entity match text length. This value depends on the value of the string_index_type parameter set in the original request, which is UnicodeCodePoints by default. Only returned for API versions v3.1-preview and up.

• offset (int) – The linked entity match text offset from the start of the document. The value depends on the value of the string_index_type parameter set in the original request, which is UnicodeCodePoints by default. Only returned for API versions v3.1-preview and up.

New in version v3.1-preview: The offset property.

get(key, default=None)

has_key(k)

items()

keys()

update(*args, **kwargs)

values()

class azure.ai.textanalytics.TextDocumentBatchStatistics(**kwargs)

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.

Variables

• document_count (int) – Number of documents submitted in the request.

• valid_document_count (int) – Number of valid documents. This excludes empty, over-size limit or non-supported languages documents.

• erroneous_document_count (int) – Number of invalid documents. This includes empty, over-size limit or non-supported languages documents.

• transaction_count (long) – Number of transactions for the request.

get(key, default=None)

has_key(k)

items()

keys()

update(*args, **kwargs)

values()

class azure.ai.textanalytics.SentenceSentiment(**kwargs)

SentenceSentiment contains the predicted sentiment and confidence scores for each individual sentence in the document.

Variables

• text (str) – The sentence text.

• sentiment (str) – The predicted Sentiment for the sentence. Possible values include: ‘positive’, ‘neutral’, ‘negative’

• confidence_scores (SentimentConfidenceScores) – The sentiment confidence score between 0 and 1 for the sentence for all labels.

• length (int) – The sentence text length. This value depends on the value of the string_index_type parameter set in the original request, which is UnicodeCodePoints by default. Only returned for API versions v3.1-preview and up.

• offset (int) – The sentence text offset from the start of the document. The value depends on the value of the string_index_type parameter set in the original request, which is UnicodeCodePoints by default. Only returned for API versions v3.1-preview and up.

• mined_opinions (list[MinedOpinion]) – The list of opinions mined from this sentence. For example in the sentence “The food is good, but the service is bad”, we would mine the two opinions “food is good” and “service is bad”. Only returned if show_opinion_mining is set to True in the call to analyze_sentiment and api version is v3.1-preview and up.

New in version v3.1-preview: The offset and mined_opinions properties.

get(key, default=None)

has_key(k)

items()

keys()

update(*args, **kwargs)

values()

class azure.ai.textanalytics.SentimentConfidenceScores(**kwargs)

The confidence scores (Softmax scores) between 0 and 1. Higher values indicate higher confidence.

Variables

• positive (float) – Positive score.

• neutral (float) – Neutral score.

• negative (float) – Negative score.

get(key, default=None)

has_key(k)

items()

keys()

update(*args, **kwargs)

values()

class azure.ai.textanalytics.MinedOpinion(**kwargs)

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.

Variables

• target (TargetSentiment) – The target of an opinion about a product/service.

• assessments (list[AssessmentSentiment]) – The assessments representing the opinion of the target.

get(key, default=None)

has_key(k)

items()

keys()

update(*args, **kwargs)

values()

class azure.ai.textanalytics.TargetSentiment(**kwargs)

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”.

Variables

• text (str) – The text value of the target.

• sentiment (str) – The predicted Sentiment for the target. Possible values include ‘positive’, ‘mixed’, and ‘negative’.

• confidence_scores (SentimentConfidenceScores) – The sentiment confidence score between 0 and 1 for the target for ‘positive’ and ‘negative’ labels. It’s score for ‘neutral’ will always be 0

• length (int) – The target text length. This value depends on the value of the string_index_type parameter set in the original request, which is UnicodeCodePoints by default.

• offset (int) – The target text offset from the start of the document. The value depends on the value of the string_index_type parameter set in the original request, which is UnicodeCodePoints by default.

get(key, default=None)

has_key(k)

items()

keys()

update(*args, **kwargs)

values()

class azure.ai.textanalytics.AssessmentSentiment(**kwargs)

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’.

Variables

• text (str) – The assessment text.

• sentiment (str) – The predicted Sentiment for the assessment. Possible values include ‘positive’, ‘mixed’, and ‘negative’.

• confidence_scores (SentimentConfidenceScores) – The sentiment confidence score between 0 and 1 for the assessment for ‘positive’ and ‘negative’ labels. It’s score for ‘neutral’ will always be 0

• length (int) – The assessment text length. This value depends on the value of the string_index_type parameter set in the original request, which is UnicodeCodePoints by default.

• offset (int) – The assessment text offset from the start of the document. The value depends on the value of the string_index_type parameter set in the original request, which is UnicodeCodePoints by default.

• is_negated (bool) – Whether the value of the assessment is negated. For example, in “The food is not good”, the assessment “good” is negated.

get(key, default=None)

has_key(k)

items()

keys()

update(*args, **kwargs)

values()

class azure.ai.textanalytics.RecognizePiiEntitiesResult(**kwargs)

RecognizePiiEntitiesResult is a result object which contains the recognized Personally Identifiable Information (PII) entities from a particular document.

Variables

• id (str) – Unique, non-empty document identifier that matches the document id that was passed in with the request. If not specified in the request, an id is assigned for the document.

• entities (list[PiiEntity]) – Recognized PII entities in the document.

• redacted_text (str) – Returns the text of the input document with all of the PII information redacted out. Only returned for API versions v3.1-preview and up.

• warnings (list[TextAnalyticsWarning]) – Warnings encountered while processing document. Results will still be returned if there are warnings, but they may not be fully accurate.

• statistics (TextDocumentStatistics) – If show_stats=True was specified in the request this field will contain information about the document payload.

• is_error (bool) – Boolean check for error item when iterating over list of results. Always False for an instance of a RecognizePiiEntitiesResult.

New in version v3.1-preview: The redacted_text parameter.

get(key, default=None)

has_key(k)

items()

keys()

update(*args, **kwargs)

values()

class azure.ai.textanalytics.PiiEntity(**kwargs)

PiiEntity contains information about a Personally Identifiable Information (PII) entity found in text.

Variables

• text (str) – Entity text as appears in the request.

• category (str) – Entity category, such as Financial Account Identification/Social Security Number/Phone Number, etc.

• subcategory (str) – Entity subcategory, such as Credit Card/EU Phone number/ABA Routing Numbers, etc.

• length (int) – The PII entity text length. This value depends on the value of the string_index_type parameter specified in the original request, which is UnicodeCodePoints by default.

• offset (int) – The PII entity text offset from the start of the document. This value depends on the value of the string_index_type parameter specified in the original request, which is UnicodeCodePoints by default.

• confidence_score (float) – Confidence score between 0 and 1 of the extracted entity.

get(key, default=None)

has_key(k)

items()

keys()

update(*args, **kwargs)

values()

class azure.ai.textanalytics.PiiEntityDomainType

The different domains of PII entities that users can filter by

PROTECTED_HEALTH_INFORMATION = 'phi'

class azure.ai.textanalytics.AnalyzeHealthcareEntitiesResultItem(**kwargs)

AnalyzeHealthcareEntitiesResultItem contains the Healthcare entities from a particular document.

Variables

• id (str) – Unique, non-empty document identifier that matches the document id that was passed in with the request. If not specified in the request, an id is assigned for the document.

• entities (list[HealthcareEntity]) – Identified Healthcare entities in the document, i.e. in the document “The subject took ibuprofen”, “ibuprofen” is an identified entity from the document.

• entity_relations (list[HealthcareRelation]) – Identified Healthcare relations between entities. For example, in the document “The subject took 100mg of ibuprofen”, we would identify the relationship between the dosage of 100mg and the medication ibuprofen.

• warnings (list[TextAnalyticsWarning]) – Warnings encountered while processing document. Results will still be returned if there are warnings, but they may not be fully accurate.

• statistics (TextDocumentStatistics) – If show_stats=true was specified in the request this field will contain information about the document payload.

• is_error (bool) – Boolean check for error item when iterating over list of results. Always False for an instance of a AnalyzeHealthcareEntitiesResultItem.

get(key, default=None)

has_key(k)

items()

keys()

update(*args, **kwargs)

values()

class azure.ai.textanalytics.AnalyzeHealthcareEntitiesResult(*args, **kwargs)

Return an iterator of items.

args and kwargs will be passed to the PageIterator constructor directly, except page_iterator_class

by_page(continuation_token: Optional[str] = None) → Iterator[Iterator[ReturnType]]

Get an iterator of pages of objects, instead of an iterator of objects.

Parameters

continuation_token (str) – An opaque continuation token. This value can be retrieved from the continuation_token field of a previous generator object. If specified, this generator will begin returning results from this point.

Returns

An iterator of pages (themselves iterator of objects)

next()

Return the next item from the iterator. When exhausted, raise StopIteration

class azure.ai.textanalytics.HealthcareEntity(**kwargs)

HealthcareEntity contains information about a Healthcare entity found in text.

Variables

• text (str) – Entity text as appears in the document.

• normalized_text (str) – Optional. Normalized version of the raw text we extract from the document. Not all `text`s have a normalized version.

• category (str) – Entity category, see the following link for health’s named entity types: https://aka.ms/text-analytics-health-entities

• subcategory (str) – Entity subcategory.

• assertion (HealthcareEntityAssertion) – Contains various assertions about this entity. 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?

• length (int) – The entity text length. This value depends on the value of the string_index_type parameter specified in the original request, which is UnicodeCodePoints by default.

• offset (int) – The entity text offset from the start of the document. This value depends on the value of the string_index_type parameter specified in the original request, which is UnicodeCodePoints by default.

• confidence_score (float) – Confidence score between 0 and 1 of the extracted entity.

• data_sources (list[HealthcareEntityDataSource]) – A collection of entity references in known data sources.

get(key, default=None)

has_key(k)

items()

keys()

update(*args, **kwargs)

values()

class azure.ai.textanalytics.HealthcareEntityDataSource(**kwargs)

HealthcareEntityDataSource contains information representing an entity reference in a known data source.

Variables

• entity_id (str) – ID of the entity in the given source catalog.

• name (str) – The name of the entity catalog from where the entity was identified, such as UMLS, CHV, MSH, etc.

get(key, default=None)

has_key(k)

items()

keys()

update(*args, **kwargs)

values()

class azure.ai.textanalytics.RecognizeEntitiesAction(**kwargs)

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 multiple long running actions on the input of documents, call method recognize_entities instead of interfacing with this model.

Keyword Arguments

• model_version (str) – The model version to use for the analysis.

• string_index_type (str) – Specifies the method used to interpret string offsets. UnicodeCodePoint, the Python encoding, is the default. To override the Python default, you can also pass in Utf16CodePoint or TextElement_v8`. For additional information see https://aka.ms/text-analytics-offsets

• disable_service_logs (bool) – If set to true, you opt-out of having your text input logged on the service side for troubleshooting. By default, Text Analytics logs your input text for 48 hours, solely to allow for troubleshooting issues in providing you with the Text Analytics natural language processing functions. Setting this parameter to true, disables input logging and may limit our ability to remediate issues that occur. Please see Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for additional details, and Microsoft Responsible AI principles at https://www.microsoft.com/ai/responsible-ai.

Variables

• model_version (str) – The model version to use for the analysis.

• string_index_type (str) – Specifies the method used to interpret string offsets. UnicodeCodePoint, the Python encoding, is the default. To override the Python default, you can also pass in Utf16CodePoint or TextElement_v8`. For additional information see https://aka.ms/text-analytics-offsets

• disable_service_logs (bool) – If set to true, you opt-out of having your text input logged on the service side for troubleshooting. By default, Text Analytics logs your input text for 48 hours, solely to allow for troubleshooting issues in providing you with the Text Analytics natural language processing functions. Setting this parameter to true, disables input logging and may limit our ability to remediate issues that occur. Please see Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for additional details, and Microsoft Responsible AI principles at https://www.microsoft.com/ai/responsible-ai.

get(key, default=None)

has_key(k)

items()

keys()

to_generated()

update(*args, **kwargs)

values()

class azure.ai.textanalytics.RecognizeLinkedEntitiesAction(**kwargs)

RecognizeLinkedEntitiesAction 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 multiple long running actions on the input of documents, call method recognize_linked_entities instead of interfacing with this model.

Keyword Arguments

• model_version (str) – The model version to use for the analysis.

• string_index_type (str) – Specifies the method used to interpret string offsets. UnicodeCodePoint, the Python encoding, is the default. To override the Python default, you can also pass in Utf16CodePoint or TextElement_v8`. For additional information see https://aka.ms/text-analytics-offsets

• disable_service_logs (bool) – If set to true, you opt-out of having your text input logged on the service side for troubleshooting. By default, Text Analytics logs your input text for 48 hours, solely to allow for troubleshooting issues in providing you with the Text Analytics natural language processing functions. Setting this parameter to true, disables input logging and may limit our ability to remediate issues that occur. Please see Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for additional details, and Microsoft Responsible AI principles at https://www.microsoft.com/ai/responsible-ai.

Variables

• model_version (str) – The model version to use for the analysis.

• string_index_type (str) – Specifies the method used to interpret string offsets. UnicodeCodePoint, the Python encoding, is the default. To override the Python default, you can also pass in Utf16CodePoint or TextElement_v8`. For additional information see https://aka.ms/text-analytics-offsets

• disable_service_logs (bool) – If set to true, you opt-out of having your text input logged on the service side for troubleshooting. By default, Text Analytics logs your input text for 48 hours, solely to allow for troubleshooting issues in providing you with the Text Analytics natural language processing functions. Setting this parameter to true, disables input logging and may limit our ability to remediate issues that occur. Please see Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for additional details, and Microsoft Responsible AI principles at https://www.microsoft.com/ai/responsible-ai.

get(key, default=None)

has_key(k)

items()

keys()

to_generated()

update(*args, **kwargs)

values()

class azure.ai.textanalytics.RecognizePiiEntitiesAction(**kwargs)

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 multiple long running actions on the input of documents, call method recognize_pii_entities instead of interfacing with this model.

Keyword Arguments

• model_version (str) – The model version to use for the analysis.

• domain_filter (str) – An optional string to set the PII domain to include only a subset of the PII entity categories. Possible values include ‘phi’ or None.

• string_index_type (str) – Specifies the method used to interpret string offsets. UnicodeCodePoint, the Python encoding, is the default. To override the Python default, you can also pass in Utf16CodePoint or TextElement_v8`. For additional information see https://aka.ms/text-analytics-offsets

• disable_service_logs (bool) – If set to true, you opt-out of having your text input logged on the service side for troubleshooting. By default, Text Analytics logs your input text for 48 hours, solely to allow for troubleshooting issues in providing you with the Text Analytics natural language processing functions. Setting this parameter to true, disables input logging and may limit our ability to remediate issues that occur. Please see Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for additional details, and Microsoft Responsible AI principles at https://www.microsoft.com/ai/responsible-ai.

Variables

• model_version (str) – The model version to use for the analysis.

• domain_filter (str) – An optional string to set the PII domain to include only a subset of the PII entity categories. Possible values include ‘phi’ or None.

• string_index_type (str) – Specifies the method used to interpret string offsets. UnicodeCodePoint, the Python encoding, is the default. To override the Python default, you can also pass in Utf16CodePoint or TextElement_v8`. For additional information see https://aka.ms/text-analytics-offsets

• disable_service_logs (bool) – If set to true, you opt-out of having your text input logged on the service side for troubleshooting. By default, Text Analytics logs your input text for 48 hours, solely to allow for troubleshooting issues in providing you with the Text Analytics natural language processing functions. Setting this parameter to true, disables input logging and may limit our ability to remediate issues that occur. Please see Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for additional details, and Microsoft Responsible AI principles at https://www.microsoft.com/ai/responsible-ai.

get(key, default=None)

has_key(k)

items()

keys()

to_generated()

update(*args, **kwargs)

values()

class azure.ai.textanalytics.ExtractKeyPhrasesAction(**kwargs)

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 multiple long running actions on the input of documents, call method extract_key_phrases instead of interfacing with this model.

Keyword Arguments

• model_version (str) – The model version to use for the analysis.

• disable_service_logs (bool) – If set to true, you opt-out of having your text input logged on the service side for troubleshooting. By default, Text Analytics logs your input text for 48 hours, solely to allow for troubleshooting issues in providing you with the Text Analytics natural language processing functions. Setting this parameter to true, disables input logging and may limit our ability to remediate issues that occur. Please see Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for additional details, and Microsoft Responsible AI principles at https://www.microsoft.com/ai/responsible-ai.

Variables

• model_version (str) – The model version to use for the analysis.

• disable_service_logs (bool) – If set to true, you opt-out of having your text input logged on the service side for troubleshooting. By default, Text Analytics logs your input text for 48 hours, solely to allow for troubleshooting issues in providing you with the Text Analytics natural language processing functions. Setting this parameter to true, disables input logging and may limit our ability to remediate issues that occur. Please see Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for additional details, and Microsoft Responsible AI principles at https://www.microsoft.com/ai/responsible-ai.

get(key, default=None)

has_key(k)

items()

keys()

to_generated()

update(*args, **kwargs)

values()

class azure.ai.textanalytics.AnalyzeActionsResult(**kwargs)

AnalyzeActionsResult contains the results of a recognize entities action on a list of documents. Returned by begin_analyze_actions

Variables

• document_results (list[RecognizeEntitiesResult]) – A list of objects containing results for all Entity Recognition actions included in the analysis.

• is_error (bool) – Boolean check for error item when iterating over list of actions. Always False for an instance of a AnalyzeActionsResult.

• action_type (str or AnalyzeActionsType) – The type of action this class is a result of.

• completed_on (datetime) – Date and time (UTC) when the result completed on the service.

• statistics (RequestStatistics) – Overall statistics for the action result.

get(key, default=None)

has_key(k)

items()

keys()

update(*args, **kwargs)

values()

class azure.ai.textanalytics.RequestStatistics(**kwargs)

get(key, default=None)

has_key(k)

items()

keys()

update(*args, **kwargs)

values()

class azure.ai.textanalytics.AnalyzeActionsType

The type of action that was applied to the documents

ANALYZE_SENTIMENT = 'analyze_sentiment'

Sentiment Analysis action.

EXTRACT_KEY_PHRASES = 'extract_key_phrases'

Key Phrase Extraction action.

RECOGNIZE_ENTITIES = 'recognize_entities'

Entities Recognition action.

RECOGNIZE_LINKED_ENTITIES = 'recognize_linked_entities'

Linked Entities Recognition action.

RECOGNIZE_PII_ENTITIES = 'recognize_pii_entities'

PII Entities Recognition action.

class azure.ai.textanalytics.AnalyzeActionsError(**kwargs)

AnalyzeActionsError is an error object which represents an an error response for an action.

Variables

• error (TextAnalyticsError) – The action result error.

• is_error (bool) – Boolean check for error item when iterating over list of results. Always True for an instance of a DocumentError.

get(key, default=None)

has_key(k)

items()

keys()

update(*args, **kwargs)

values()

azure.ai.textanalytics.PiiEntityCategoryType

alias of azure.ai.textanalytics._generated.v3_1_preview_5.models._text_analytics_client_enums.PiiCategory

azure.ai.textanalytics.HealthcareEntityRelationType

alias of azure.ai.textanalytics._generated.v3_1_preview_5.models._text_analytics_client_enums.RelationType

class azure.ai.textanalytics.HealthcareEntityRelationRoleType

Type of roles entities can have in entity_relations. There may be roles not covered in this enum

ABBREVIATED_TERM = 'AbbreviatedTerm'

BODY_STRUCTURE = 'BodyStructure'

CONDITION = 'Condition'

DIRECTION = 'Direction'

DOSAGE = 'Dosage'

EVENT = 'Event'

EXAMINATION = 'Examination'

FORM = 'Form'

FREQUENCY = 'Frequency'

FULL_TERM = 'FullTerm'

MEDICATION = 'Medication'

QUALIFIER = 'Qualifier'

RELATION = 'Relation'

ROUTE = 'Route'

TIME = 'Time'

TREATMENT = 'Treatment'

UNIT = 'Unit'

VALUE = 'Value'

class azure.ai.textanalytics.HealthcareRelation(**kwargs)

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.

Variables

• relation_type (str or HealthcareEntityRelationType) – The type of relation, i.e. the relationship between “100mg” and “ibuprofen” in the document “The subject took 100 mg of ibuprofen” is “DosageOfMedication”.

• roles (list[HealthcareRelationRole]) – The roles present in this relation. I.e., in the document “The subject took 100 mg of ibuprofen”, the present roles are “Dosage” and “Medication”.

get(key, default=None)

has_key(k)

items()

keys()

update(*args, **kwargs)

values()

class azure.ai.textanalytics.HealthcareRelationRole(**kwargs)

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”.

Variables

• name (str or HealthcareEntityRelationRoleType) – The role of the entity in the relationship. I.e., in the relation “The subject took 100 mg of ibuprofen”, the dosage entity “100 mg” has role “Dosage”.

• entity (HealthcareEntity) – The entity that is present in the relationship. For example, in “The subject took 100 mg of ibuprofen”, this property holds the dosage entity of “100 mg”.

get(key, default=None)

has_key(k)

items()

keys()

update(*args, **kwargs)

values()

class azure.ai.textanalytics.HealthcareEntityAssertion(**kwargs)

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?

Variables

• conditionality (str) – Describes whether the healthcare entity it’s on is conditional on another entity. For example, “If the patient has a fever, he has pneumonia”, the diagnosis of pneumonia is ‘conditional’ on whether the patient has a fever. Possible values are “hypothetical” and “conditional”.

• certainty (str) – Describes how certain the healthcare entity it’s on is. For example, in “The patient may have a fever”, the fever entity is not 100% certain, but is instead “positivePossible”. Possible values are “positive”, “positivePossible”, “neutralPossible”, “negativePossible”, and “negative”.

• association (str) – Describes whether the healthcare entity it’s on is the subject of the document, or if this entity describes someone else in the document. For example, in “The subject’s mother has a fever”, the “fever” entity is not associated with the subject themselves, but with the subject’s mother. Possible values are “subject” and “other”.

get(key, default=None)

has_key(k)

items()

keys()

update(*args, **kwargs)

values()

azure.ai.textanalytics.EntityConditionality

alias of azure.ai.textanalytics._generated.v3_1_preview_5.models._text_analytics_client_enums.Conditionality

azure.ai.textanalytics.EntityCertainty

alias of azure.ai.textanalytics._generated.v3_1_preview_5.models._text_analytics_client_enums.Certainty

azure.ai.textanalytics.EntityAssociation

alias of azure.ai.textanalytics._generated.v3_1_preview_5.models._text_analytics_client_enums.Association

class azure.ai.textanalytics.AnalyzeSentimentAction(**kwargs)

AnalyzeSentimentAction encapsulates the parameters for starting a long-running Sentiment Analysis operation.

If you just want to analyze sentiment in a list of documents, and not perform multiple long running actions on the input of documents, call method analyze_sentiment instead of interfacing with this model.

Keyword Arguments

• model_version (str) – The model version to use for the analysis.

• show_opinion_mining (bool) – Whether to mine the opinions of a sentence and conduct more granular analysis around the aspects of a product or service (also known as aspect-based sentiment analysis). If set to true, the returned SentenceSentiment objects will have property mined_opinions containing the result of this analysis.

• string_index_type (str) – Specifies the method used to interpret string offsets. UnicodeCodePoint, the Python encoding, is the default. To override the Python default, you can also pass in Utf16CodePoint or TextElement_v8`. For additional information see https://aka.ms/text-analytics-offsets

• disable_service_logs (bool) – If set to true, you opt-out of having your text input logged on the service side for troubleshooting. By default, Text Analytics logs your input text for 48 hours, solely to allow for troubleshooting issues in providing you with the Text Analytics natural language processing functions. Setting this parameter to true, disables input logging and may limit our ability to remediate issues that occur. Please see Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for additional details, and Microsoft Responsible AI principles at https://www.microsoft.com/ai/responsible-ai.

Variables

• model_version (str) – The model version to use for the analysis.

• show_opinion_mining (bool) – Whether to mine the opinions of a sentence and conduct more granular analysis around the aspects of a product or service (also known as aspect-based sentiment analysis). If set to true, the returned SentenceSentiment objects will have property mined_opinions containing the result of this analysis.

• string_index_type (str) – Specifies the method used to interpret string offsets. UnicodeCodePoint, the Python encoding, is the default. To override the Python default, you can also pass in Utf16CodePoint or TextElement_v8`. For additional information see https://aka.ms/text-analytics-offsets

• disable_service_logs (bool) – If set to true, you opt-out of having your text input logged on the service side for troubleshooting. By default, Text Analytics logs your input text for 48 hours, solely to allow for troubleshooting issues in providing you with the Text Analytics natural language processing functions. Setting this parameter to true, disables input logging and may limit our ability to remediate issues that occur. Please see Cognitive Services Compliance and Privacy notes at https://aka.ms/cs-compliance for additional details, and Microsoft Responsible AI principles at https://www.microsoft.com/ai/responsible-ai.

get(key, default=None)

has_key(k)

items()

keys()

to_generated()

update(*args, **kwargs)

values()

Subpackages

• azure.ai.textanalytics.aio package