def analyze_entity_sentiment(
self,
request: language_service.AnalyzeEntitySentimentRequest = None,
*,
document: language_service.Document = None,
encoding_type: language_service.EncodingType = None,
retry: retries.Retry = gapic_v1.method.DEFAULT,
timeout: float = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> language_service.AnalyzeEntitySentimentResponse:
r"""Finds entities, similar to
[AnalyzeEntities][google.cloud.language.v1.LanguageService.AnalyzeEntities]
in the text and analyzes sentiment associated with each entity
and its mentions.
Args:
request (google.cloud.language_v1.types.AnalyzeEntitySentimentRequest):
The request object. The entity-level sentiment analysis
request message.
document (google.cloud.language_v1.types.Document):
Input document.
This corresponds to the ``document`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
encoding_type (google.cloud.language_v1.types.EncodingType):
The encoding type used by the API to
calculate offsets.
This corresponds to the ``encoding_type`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
retry (google.api_core.retry.Retry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.cloud.language_v1.types.AnalyzeEntitySentimentResponse:
The entity-level sentiment analysis
response message.
"""
# Create or coerce a protobuf request object.
# Sanity check: If we got a request object, we should *not* have
# gotten any keyword arguments that map to the request.
has_flattened_params = any([document, encoding_type])
if request is not None and has_flattened_params:
raise ValueError("If the `request` argument is set, then none of "
"the individual field arguments should be set.")
# Minor optimization to avoid making a copy if the user passes
# in a language_service.AnalyzeEntitySentimentRequest.
# There's no risk of modifying the input as we've already verified
# there are no flattened fields.
if not isinstance(request,
language_service.AnalyzeEntitySentimentRequest):
request = language_service.AnalyzeEntitySentimentRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if document is not None:
request.document = document
if encoding_type is not None:
request.encoding_type = encoding_type
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = self._transport._wrapped_methods[
self._transport.analyze_entity_sentiment]
# Send the request.
response = rpc(
request,
retry=retry,
timeout=timeout,
metadata=metadata,
)
# Done; return the response.
return response
async def analyze_entity_sentiment(
self,
request: language_service.AnalyzeEntitySentimentRequest = None,
*,
document: language_service.Document = None,
encoding_type: language_service.EncodingType = None,
retry: retries.Retry = gapic_v1.method.DEFAULT,
timeout: float = None,
metadata: Sequence[Tuple[str, str]] = (),
) -> language_service.AnalyzeEntitySentimentResponse:
r"""Finds entities, similar to
[AnalyzeEntities][google.cloud.language.v1.LanguageService.AnalyzeEntities]
in the text and analyzes sentiment associated with each entity
and its mentions.
Args:
request (:class:`google.cloud.language_v1.types.AnalyzeEntitySentimentRequest`):
The request object. The entity-level sentiment analysis
request message.
document (:class:`google.cloud.language_v1.types.Document`):
Input document.
This corresponds to the ``document`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
encoding_type (:class:`google.cloud.language_v1.types.EncodingType`):
The encoding type used by the API to
calculate offsets.
This corresponds to the ``encoding_type`` field
on the ``request`` instance; if ``request`` is provided, this
should not be set.
retry (google.api_core.retry.Retry): Designation of what errors, if any,
should be retried.
timeout (float): The timeout for this request.
metadata (Sequence[Tuple[str, str]]): Strings which should be
sent along with the request as metadata.
Returns:
google.cloud.language_v1.types.AnalyzeEntitySentimentResponse:
The entity-level sentiment analysis
response message.
"""
# Create or coerce a protobuf request object.
# Sanity check: If we got a request object, we should *not* have
# gotten any keyword arguments that map to the request.
has_flattened_params = any([document, encoding_type])
if request is not None and has_flattened_params:
raise ValueError(
"If the `request` argument is set, then none of "
"the individual field arguments should be set."
)
request = language_service.AnalyzeEntitySentimentRequest(request)
# If we have keyword arguments corresponding to fields on the
# request, apply these.
if document is not None:
request.document = document
if encoding_type is not None:
request.encoding_type = encoding_type
# Wrap the RPC method; this adds retry and timeout information,
# and friendly error handling.
rpc = gapic_v1.method_async.wrap_method(
self._client._transport.analyze_entity_sentiment,
default_retry=retries.Retry(
initial=0.1,
maximum=60.0,
multiplier=1.3,
predicate=retries.if_exception_type(
exceptions.DeadlineExceeded, exceptions.ServiceUnavailable,
),
),
default_timeout=600.0,
client_info=DEFAULT_CLIENT_INFO,
)
# Send the request.
response = await rpc(request, retry=retry, timeout=timeout, metadata=metadata,)
# Done; return the response.
return response