analyzeEntities(body, x__xgafv=None)
Finds named entities (currently proper names and common nouns) in the text
analyzeEntitySentiment(body, x__xgafv=None)
Finds entities, similar to AnalyzeEntities in the text and analyzes
analyzeSentiment(body, x__xgafv=None)
Analyzes the sentiment of the provided text.
analyzeSyntax(body, x__xgafv=None)
Analyzes the syntax of the text and provides sentence boundaries and
annotateText(body, x__xgafv=None)
A convenience method that provides all syntax, sentiment, entity, and
classifyText(body, x__xgafv=None)
Classifies a document into categories.
analyzeEntities(body, x__xgafv=None)
Finds named entities (currently proper names and common nouns) in the text
along with entity types, salience, mentions for each entity, and
other properties.
Args:
body: object, The request body. (required)
The object takes the form of:
{ # The entity analysis request message.
"document": { # ################################################################ # # Input document.
#
# Represents the input to API methods.
"content": "A String", # The content of the input in string format.
# Cloud audit logging exempt since it is based on user data.
"type": "A String", # Required. If the type is not set or is `TYPE_UNSPECIFIED`,
# returns an `INVALID_ARGUMENT` error.
"language": "A String", # The language of the document (if not specified, the language is
# automatically detected). Both ISO and BCP-47 language codes are
# accepted.
# [Language Support](/natural-language/docs/languages)
# lists currently supported languages for each API method.
# If the language (either specified by the caller or automatically detected)
# is not supported by the called API method, an `INVALID_ARGUMENT` error
# is returned.
"gcsContentUri": "A String", # The Google Cloud Storage URI where the file content is located.
# This URI must be of the form: gs://bucket_name/object_name. For more
# details, see https://cloud.google.com/storage/docs/reference-uris.
# NOTE: Cloud Storage object versioning is not supported.
},
"encodingType": "A String", # The encoding type used by the API to calculate offsets.
}
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # The entity analysis response message.
"entities": [ # The recognized entities in the input document.
{ # Represents a phrase in the text that is a known entity, such as
# a person, an organization, or location. The API associates information, such
# as salience and mentions, with entities.
"name": "A String", # The representative name for the entity.
"sentiment": { # Represents the feeling associated with the entire text or entities in # For calls to AnalyzeEntitySentiment or if
# AnnotateTextRequest.Features.extract_entity_sentiment is set to
# true, this field will contain the aggregate sentiment expressed for this
# entity in the provided document.
# the text.
# Next ID: 6
"score": 3.14, # Sentiment score between -1.0 (negative sentiment) and 1.0
# (positive sentiment).
"magnitude": 3.14, # A non-negative number in the [0, +inf) range, which represents
# the absolute magnitude of sentiment regardless of score (positive or
# negative).
},
"salience": 3.14, # The salience score associated with the entity in the [0, 1.0] range.
#
# The salience score for an entity provides information about the
# importance or centrality of that entity to the entire document text.
# Scores closer to 0 are less salient, while scores closer to 1.0 are highly
# salient.
"mentions": [ # The mentions of this entity in the input document. The API currently
# supports proper noun mentions.
{ # Represents a mention for an entity in the text. Currently, proper noun
# mentions are supported.
"text": { # Represents an output piece of text. # The mention text.
"content": "A String", # The content of the output text.
"beginOffset": 42, # The API calculates the beginning offset of the content in the original
# document according to the EncodingType specified in the API request.
},
"type": "A String", # The type of the entity mention.
"sentiment": { # Represents the feeling associated with the entire text or entities in # For calls to AnalyzeEntitySentiment or if
# AnnotateTextRequest.Features.extract_entity_sentiment is set to
# true, this field will contain the sentiment expressed for this mention of
# the entity in the provided document.
# the text.
# Next ID: 6
"score": 3.14, # Sentiment score between -1.0 (negative sentiment) and 1.0
# (positive sentiment).
"magnitude": 3.14, # A non-negative number in the [0, +inf) range, which represents
# the absolute magnitude of sentiment regardless of score (positive or
# negative).
},
},
],
"type": "A String", # The entity type.
"metadata": { # Metadata associated with the entity.
#
# For most entity types, the metadata is a Wikipedia URL (`wikipedia_url`)
# and Knowledge Graph MID (`mid`), if they are available. For the metadata
# associated with other entity types, see the Type table below.
"a_key": "A String",
},
},
],
"language": "A String", # The language of the text, which will be the same as the language specified
# in the request or, if not specified, the automatically-detected language.
# See Document.language field for more details.
}
analyzeEntitySentiment(body, x__xgafv=None)
Finds entities, similar to AnalyzeEntities in the text and analyzes
sentiment associated with each entity and its mentions.
Args:
body: object, The request body. (required)
The object takes the form of:
{ # The entity-level sentiment analysis request message.
"encodingType": "A String", # The encoding type used by the API to calculate offsets.
"document": { # ################################################################ # # Input document.
#
# Represents the input to API methods.
"content": "A String", # The content of the input in string format.
# Cloud audit logging exempt since it is based on user data.
"type": "A String", # Required. If the type is not set or is `TYPE_UNSPECIFIED`,
# returns an `INVALID_ARGUMENT` error.
"language": "A String", # The language of the document (if not specified, the language is
# automatically detected). Both ISO and BCP-47 language codes are
# accepted.
# [Language Support](/natural-language/docs/languages)
# lists currently supported languages for each API method.
# If the language (either specified by the caller or automatically detected)
# is not supported by the called API method, an `INVALID_ARGUMENT` error
# is returned.
"gcsContentUri": "A String", # The Google Cloud Storage URI where the file content is located.
# This URI must be of the form: gs://bucket_name/object_name. For more
# details, see https://cloud.google.com/storage/docs/reference-uris.
# NOTE: Cloud Storage object versioning is not supported.
},
}
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # The entity-level sentiment analysis response message.
"entities": [ # The recognized entities in the input document with associated sentiments.
{ # Represents a phrase in the text that is a known entity, such as
# a person, an organization, or location. The API associates information, such
# as salience and mentions, with entities.
"name": "A String", # The representative name for the entity.
"sentiment": { # Represents the feeling associated with the entire text or entities in # For calls to AnalyzeEntitySentiment or if
# AnnotateTextRequest.Features.extract_entity_sentiment is set to
# true, this field will contain the aggregate sentiment expressed for this
# entity in the provided document.
# the text.
# Next ID: 6
"score": 3.14, # Sentiment score between -1.0 (negative sentiment) and 1.0
# (positive sentiment).
"magnitude": 3.14, # A non-negative number in the [0, +inf) range, which represents
# the absolute magnitude of sentiment regardless of score (positive or
# negative).
},
"salience": 3.14, # The salience score associated with the entity in the [0, 1.0] range.
#
# The salience score for an entity provides information about the
# importance or centrality of that entity to the entire document text.
# Scores closer to 0 are less salient, while scores closer to 1.0 are highly
# salient.
"mentions": [ # The mentions of this entity in the input document. The API currently
# supports proper noun mentions.
{ # Represents a mention for an entity in the text. Currently, proper noun
# mentions are supported.
"text": { # Represents an output piece of text. # The mention text.
"content": "A String", # The content of the output text.
"beginOffset": 42, # The API calculates the beginning offset of the content in the original
# document according to the EncodingType specified in the API request.
},
"type": "A String", # The type of the entity mention.
"sentiment": { # Represents the feeling associated with the entire text or entities in # For calls to AnalyzeEntitySentiment or if
# AnnotateTextRequest.Features.extract_entity_sentiment is set to
# true, this field will contain the sentiment expressed for this mention of
# the entity in the provided document.
# the text.
# Next ID: 6
"score": 3.14, # Sentiment score between -1.0 (negative sentiment) and 1.0
# (positive sentiment).
"magnitude": 3.14, # A non-negative number in the [0, +inf) range, which represents
# the absolute magnitude of sentiment regardless of score (positive or
# negative).
},
},
],
"type": "A String", # The entity type.
"metadata": { # Metadata associated with the entity.
#
# For most entity types, the metadata is a Wikipedia URL (`wikipedia_url`)
# and Knowledge Graph MID (`mid`), if they are available. For the metadata
# associated with other entity types, see the Type table below.
"a_key": "A String",
},
},
],
"language": "A String", # The language of the text, which will be the same as the language specified
# in the request or, if not specified, the automatically-detected language.
# See Document.language field for more details.
}
analyzeSentiment(body, x__xgafv=None)
Analyzes the sentiment of the provided text.
Args:
body: object, The request body. (required)
The object takes the form of:
{ # The sentiment analysis request message.
"encodingType": "A String", # The encoding type used by the API to calculate sentence offsets for the
# sentence sentiment.
"document": { # ################################################################ # # Input document.
#
# Represents the input to API methods.
"content": "A String", # The content of the input in string format.
# Cloud audit logging exempt since it is based on user data.
"type": "A String", # Required. If the type is not set or is `TYPE_UNSPECIFIED`,
# returns an `INVALID_ARGUMENT` error.
"language": "A String", # The language of the document (if not specified, the language is
# automatically detected). Both ISO and BCP-47 language codes are
# accepted.
# [Language Support](/natural-language/docs/languages)
# lists currently supported languages for each API method.
# If the language (either specified by the caller or automatically detected)
# is not supported by the called API method, an `INVALID_ARGUMENT` error
# is returned.
"gcsContentUri": "A String", # The Google Cloud Storage URI where the file content is located.
# This URI must be of the form: gs://bucket_name/object_name. For more
# details, see https://cloud.google.com/storage/docs/reference-uris.
# NOTE: Cloud Storage object versioning is not supported.
},
}
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # The sentiment analysis response message.
"documentSentiment": { # Represents the feeling associated with the entire text or entities in # The overall sentiment of the input document.
# the text.
# Next ID: 6
"score": 3.14, # Sentiment score between -1.0 (negative sentiment) and 1.0
# (positive sentiment).
"magnitude": 3.14, # A non-negative number in the [0, +inf) range, which represents
# the absolute magnitude of sentiment regardless of score (positive or
# negative).
},
"language": "A String", # The language of the text, which will be the same as the language specified
# in the request or, if not specified, the automatically-detected language.
# See Document.language field for more details.
"sentences": [ # The sentiment for all the sentences in the document.
{ # Represents a sentence in the input document.
"text": { # Represents an output piece of text. # The sentence text.
"content": "A String", # The content of the output text.
"beginOffset": 42, # The API calculates the beginning offset of the content in the original
# document according to the EncodingType specified in the API request.
},
"sentiment": { # Represents the feeling associated with the entire text or entities in # For calls to AnalyzeSentiment or if
# AnnotateTextRequest.Features.extract_document_sentiment is set to
# true, this field will contain the sentiment for the sentence.
# the text.
# Next ID: 6
"score": 3.14, # Sentiment score between -1.0 (negative sentiment) and 1.0
# (positive sentiment).
"magnitude": 3.14, # A non-negative number in the [0, +inf) range, which represents
# the absolute magnitude of sentiment regardless of score (positive or
# negative).
},
},
],
}
analyzeSyntax(body, x__xgafv=None)
Analyzes the syntax of the text and provides sentence boundaries and
tokenization along with part of speech tags, dependency trees, and other
properties.
Args:
body: object, The request body. (required)
The object takes the form of:
{ # The syntax analysis request message.
"encodingType": "A String", # The encoding type used by the API to calculate offsets.
"document": { # ################################################################ # # Input document.
#
# Represents the input to API methods.
"content": "A String", # The content of the input in string format.
# Cloud audit logging exempt since it is based on user data.
"type": "A String", # Required. If the type is not set or is `TYPE_UNSPECIFIED`,
# returns an `INVALID_ARGUMENT` error.
"language": "A String", # The language of the document (if not specified, the language is
# automatically detected). Both ISO and BCP-47 language codes are
# accepted.
# [Language Support](/natural-language/docs/languages)
# lists currently supported languages for each API method.
# If the language (either specified by the caller or automatically detected)
# is not supported by the called API method, an `INVALID_ARGUMENT` error
# is returned.
"gcsContentUri": "A String", # The Google Cloud Storage URI where the file content is located.
# This URI must be of the form: gs://bucket_name/object_name. For more
# details, see https://cloud.google.com/storage/docs/reference-uris.
# NOTE: Cloud Storage object versioning is not supported.
},
}
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # The syntax analysis response message.
"tokens": [ # Tokens, along with their syntactic information, in the input document.
{ # Represents the smallest syntactic building block of the text.
"lemma": "A String", # [Lemma](https://en.wikipedia.org/wiki/Lemma_%28morphology%29) of the token.
"dependencyEdge": { # Represents dependency parse tree information for a token. # Dependency tree parse for this token.
"headTokenIndex": 42, # Represents the head of this token in the dependency tree.
# This is the index of the token which has an arc going to this token.
# The index is the position of the token in the array of tokens returned
# by the API method. If this token is a root token, then the
# `head_token_index` is its own index.
"label": "A String", # The parse label for the token.
},
"partOfSpeech": { # Represents part of speech information for a token. # Parts of speech tag for this token.
"case": "A String", # The grammatical case.
"mood": "A String", # The grammatical mood.
"form": "A String", # The grammatical form.
"gender": "A String", # The grammatical gender.
"aspect": "A String", # The grammatical aspect.
"number": "A String", # The grammatical number.
"person": "A String", # The grammatical person.
"tag": "A String", # The part of speech tag.
"tense": "A String", # The grammatical tense.
"reciprocity": "A String", # The grammatical reciprocity.
"proper": "A String", # The grammatical properness.
"voice": "A String", # The grammatical voice.
},
"text": { # Represents an output piece of text. # The token text.
"content": "A String", # The content of the output text.
"beginOffset": 42, # The API calculates the beginning offset of the content in the original
# document according to the EncodingType specified in the API request.
},
},
],
"language": "A String", # The language of the text, which will be the same as the language specified
# in the request or, if not specified, the automatically-detected language.
# See Document.language field for more details.
"sentences": [ # Sentences in the input document.
{ # Represents a sentence in the input document.
"text": { # Represents an output piece of text. # The sentence text.
"content": "A String", # The content of the output text.
"beginOffset": 42, # The API calculates the beginning offset of the content in the original
# document according to the EncodingType specified in the API request.
},
"sentiment": { # Represents the feeling associated with the entire text or entities in # For calls to AnalyzeSentiment or if
# AnnotateTextRequest.Features.extract_document_sentiment is set to
# true, this field will contain the sentiment for the sentence.
# the text.
# Next ID: 6
"score": 3.14, # Sentiment score between -1.0 (negative sentiment) and 1.0
# (positive sentiment).
"magnitude": 3.14, # A non-negative number in the [0, +inf) range, which represents
# the absolute magnitude of sentiment regardless of score (positive or
# negative).
},
},
],
}
annotateText(body, x__xgafv=None)
A convenience method that provides all syntax, sentiment, entity, and
classification features in one call.
Args:
body: object, The request body. (required)
The object takes the form of:
{ # The request message for the text annotation API, which can perform multiple
# analysis types (sentiment, entities, and syntax) in one call.
"encodingType": "A String", # The encoding type used by the API to calculate offsets.
"document": { # ################################################################ # # Input document.
#
# Represents the input to API methods.
"content": "A String", # The content of the input in string format.
# Cloud audit logging exempt since it is based on user data.
"type": "A String", # Required. If the type is not set or is `TYPE_UNSPECIFIED`,
# returns an `INVALID_ARGUMENT` error.
"language": "A String", # The language of the document (if not specified, the language is
# automatically detected). Both ISO and BCP-47 language codes are
# accepted.
# [Language Support](/natural-language/docs/languages)
# lists currently supported languages for each API method.
# If the language (either specified by the caller or automatically detected)
# is not supported by the called API method, an `INVALID_ARGUMENT` error
# is returned.
"gcsContentUri": "A String", # The Google Cloud Storage URI where the file content is located.
# This URI must be of the form: gs://bucket_name/object_name. For more
# details, see https://cloud.google.com/storage/docs/reference-uris.
# NOTE: Cloud Storage object versioning is not supported.
},
"features": { # All available features for sentiment, syntax, and semantic analysis. # The enabled features.
# Setting each one to true will enable that specific analysis for the input.
# Next ID: 10
"extractDocumentSentiment": True or False, # Extract document-level sentiment.
"extractEntitySentiment": True or False, # Extract entities and their associated sentiment.
"extractSyntax": True or False, # Extract syntax information.
"extractEntities": True or False, # Extract entities.
"classifyText": True or False, # Classify the full document into categories. If this is true,
# the API will use the default model which classifies into a
# [predefined taxonomy](/natural-language/docs/categories).
},
}
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # The text annotations response message.
"language": "A String", # The language of the text, which will be the same as the language specified
# in the request or, if not specified, the automatically-detected language.
# See Document.language field for more details.
"tokens": [ # Tokens, along with their syntactic information, in the input document.
# Populated if the user enables
# AnnotateTextRequest.Features.extract_syntax.
{ # Represents the smallest syntactic building block of the text.
"lemma": "A String", # [Lemma](https://en.wikipedia.org/wiki/Lemma_%28morphology%29) of the token.
"dependencyEdge": { # Represents dependency parse tree information for a token. # Dependency tree parse for this token.
"headTokenIndex": 42, # Represents the head of this token in the dependency tree.
# This is the index of the token which has an arc going to this token.
# The index is the position of the token in the array of tokens returned
# by the API method. If this token is a root token, then the
# `head_token_index` is its own index.
"label": "A String", # The parse label for the token.
},
"partOfSpeech": { # Represents part of speech information for a token. # Parts of speech tag for this token.
"case": "A String", # The grammatical case.
"mood": "A String", # The grammatical mood.
"form": "A String", # The grammatical form.
"gender": "A String", # The grammatical gender.
"aspect": "A String", # The grammatical aspect.
"number": "A String", # The grammatical number.
"person": "A String", # The grammatical person.
"tag": "A String", # The part of speech tag.
"tense": "A String", # The grammatical tense.
"reciprocity": "A String", # The grammatical reciprocity.
"proper": "A String", # The grammatical properness.
"voice": "A String", # The grammatical voice.
},
"text": { # Represents an output piece of text. # The token text.
"content": "A String", # The content of the output text.
"beginOffset": 42, # The API calculates the beginning offset of the content in the original
# document according to the EncodingType specified in the API request.
},
},
],
"entities": [ # Entities, along with their semantic information, in the input document.
# Populated if the user enables
# AnnotateTextRequest.Features.extract_entities.
{ # Represents a phrase in the text that is a known entity, such as
# a person, an organization, or location. The API associates information, such
# as salience and mentions, with entities.
"name": "A String", # The representative name for the entity.
"sentiment": { # Represents the feeling associated with the entire text or entities in # For calls to AnalyzeEntitySentiment or if
# AnnotateTextRequest.Features.extract_entity_sentiment is set to
# true, this field will contain the aggregate sentiment expressed for this
# entity in the provided document.
# the text.
# Next ID: 6
"score": 3.14, # Sentiment score between -1.0 (negative sentiment) and 1.0
# (positive sentiment).
"magnitude": 3.14, # A non-negative number in the [0, +inf) range, which represents
# the absolute magnitude of sentiment regardless of score (positive or
# negative).
},
"salience": 3.14, # The salience score associated with the entity in the [0, 1.0] range.
#
# The salience score for an entity provides information about the
# importance or centrality of that entity to the entire document text.
# Scores closer to 0 are less salient, while scores closer to 1.0 are highly
# salient.
"mentions": [ # The mentions of this entity in the input document. The API currently
# supports proper noun mentions.
{ # Represents a mention for an entity in the text. Currently, proper noun
# mentions are supported.
"text": { # Represents an output piece of text. # The mention text.
"content": "A String", # The content of the output text.
"beginOffset": 42, # The API calculates the beginning offset of the content in the original
# document according to the EncodingType specified in the API request.
},
"type": "A String", # The type of the entity mention.
"sentiment": { # Represents the feeling associated with the entire text or entities in # For calls to AnalyzeEntitySentiment or if
# AnnotateTextRequest.Features.extract_entity_sentiment is set to
# true, this field will contain the sentiment expressed for this mention of
# the entity in the provided document.
# the text.
# Next ID: 6
"score": 3.14, # Sentiment score between -1.0 (negative sentiment) and 1.0
# (positive sentiment).
"magnitude": 3.14, # A non-negative number in the [0, +inf) range, which represents
# the absolute magnitude of sentiment regardless of score (positive or
# negative).
},
},
],
"type": "A String", # The entity type.
"metadata": { # Metadata associated with the entity.
#
# For most entity types, the metadata is a Wikipedia URL (`wikipedia_url`)
# and Knowledge Graph MID (`mid`), if they are available. For the metadata
# associated with other entity types, see the Type table below.
"a_key": "A String",
},
},
],
"documentSentiment": { # Represents the feeling associated with the entire text or entities in # The overall sentiment for the document. Populated if the user enables
# AnnotateTextRequest.Features.extract_document_sentiment.
# the text.
# Next ID: 6
"score": 3.14, # Sentiment score between -1.0 (negative sentiment) and 1.0
# (positive sentiment).
"magnitude": 3.14, # A non-negative number in the [0, +inf) range, which represents
# the absolute magnitude of sentiment regardless of score (positive or
# negative).
},
"sentences": [ # Sentences in the input document. Populated if the user enables
# AnnotateTextRequest.Features.extract_syntax.
{ # Represents a sentence in the input document.
"text": { # Represents an output piece of text. # The sentence text.
"content": "A String", # The content of the output text.
"beginOffset": 42, # The API calculates the beginning offset of the content in the original
# document according to the EncodingType specified in the API request.
},
"sentiment": { # Represents the feeling associated with the entire text or entities in # For calls to AnalyzeSentiment or if
# AnnotateTextRequest.Features.extract_document_sentiment is set to
# true, this field will contain the sentiment for the sentence.
# the text.
# Next ID: 6
"score": 3.14, # Sentiment score between -1.0 (negative sentiment) and 1.0
# (positive sentiment).
"magnitude": 3.14, # A non-negative number in the [0, +inf) range, which represents
# the absolute magnitude of sentiment regardless of score (positive or
# negative).
},
},
],
"categories": [ # Categories identified in the input document.
{ # Represents a category returned from the text classifier.
"confidence": 3.14, # The classifier's confidence of the category. Number represents how certain
# the classifier is that this category represents the given text.
"name": "A String", # The name of the category representing the document, from the [predefined
# taxonomy](/natural-language/docs/categories).
},
],
}
classifyText(body, x__xgafv=None)
Classifies a document into categories.
Args:
body: object, The request body. (required)
The object takes the form of:
{ # The document classification request message.
"document": { # ################################################################ # # Input document.
#
# Represents the input to API methods.
"content": "A String", # The content of the input in string format.
# Cloud audit logging exempt since it is based on user data.
"type": "A String", # Required. If the type is not set or is `TYPE_UNSPECIFIED`,
# returns an `INVALID_ARGUMENT` error.
"language": "A String", # The language of the document (if not specified, the language is
# automatically detected). Both ISO and BCP-47 language codes are
# accepted.
# [Language Support](/natural-language/docs/languages)
# lists currently supported languages for each API method.
# If the language (either specified by the caller or automatically detected)
# is not supported by the called API method, an `INVALID_ARGUMENT` error
# is returned.
"gcsContentUri": "A String", # The Google Cloud Storage URI where the file content is located.
# This URI must be of the form: gs://bucket_name/object_name. For more
# details, see https://cloud.google.com/storage/docs/reference-uris.
# NOTE: Cloud Storage object versioning is not supported.
},
}
x__xgafv: string, V1 error format.
Allowed values
1 - v1 error format
2 - v2 error format
Returns:
An object of the form:
{ # The document classification response message.
"categories": [ # Categories representing the input document.
{ # Represents a category returned from the text classifier.
"confidence": 3.14, # The classifier's confidence of the category. Number represents how certain
# the classifier is that this category represents the given text.
"name": "A String", # The name of the category representing the document, from the [predefined
# taxonomy](/natural-language/docs/categories).
},
],
}