Generated entity-sentiment samples with response handling

analyze_entity_sentiment_request_entity_sentiment_file.py

# -*- coding: utf-8 -*-
#
# Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# DO NOT EDIT! This is a generated sample ("Request",  "entity_sentiment_file")

# To install the latest published package dependency, execute the following:
#   pip install google-cloud-language

import sys

# [START language_entity_sentiment_file]

from google.cloud import language_v1
from google.cloud.language_v1 import enums
from google.cloud.language_v1 import enums
import six


def sample_analyze_entity_sentiment(gcs_uri):
    """Analyze entities and sentiment of a text file"""

    # [START language_entity_sentiment_file_core]

    client = language_v1.LanguageServiceClient()

    # gcs_uri = 'gs://cloud-samples-data/language/president.txt'

    if isinstance(gcs_uri, six.binary_type):
        gcs_uri = gcs_uri.decode('utf-8')
    type_ = enums.Document.Type.PLAIN_TEXT
    document = {'gcs_content_uri': gcs_uri, 'type': type_}

    response = client.analyze_entity_sentiment(document)
    for entity in response.entities:
        print('Name: {}'.format(entity.name))
        for mention in entity.mentions:
            print('  - Begin offset : {}'.format(mention.text.begin_offset))
            print('    Content : {}'.format(mention.text.content))
            print('    Magnitude : {}'.format(mention.sentiment.magnitude))
            print('    Sentiment : {}'.format(mention.sentiment.score))
            print('    Type : {}'.format(
                enums.EntityMention.Type(mention.type).name))
        print('  Salience : {}'.format(entity.salience))
        print('  Aggregate sentiment : {}'.format(entity.sentiment.score))
        print('  Aggregate sentiment magnitude : {}'.format(
            entity.sentiment.score))

    # [END language_entity_sentiment_file_core]


# [END language_entity_sentiment_file]


def main():
    import argparse

    parser = argparse.ArgumentParser()
    parser.add_argument(
        '--gcs_uri',
        type=str,
        default='gs://cloud-samples-data/language/president.txt')
    args = parser.parse_args()

    sample_analyze_entity_sentiment(args.gcs_uri)


if __name__ == '__main__':
    main()

analyze_entity_sentiment_request_entity_sentiment_text.py

# -*- coding: utf-8 -*-
#
# Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     https://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

# DO NOT EDIT! This is a generated sample ("Request",  "entity_sentiment_text")

# To install the latest published package dependency, execute the following:
#   pip install google-cloud-language

import sys

# [START language_entity_sentiment_text]

from google.cloud import language_v1
from google.cloud.language_v1 import enums
from google.cloud.language_v1 import enums
import six


def sample_analyze_entity_sentiment(content):
    """Analyze entities and sentiment of a text string"""

    # [START language_entity_sentiment_text_core]

    client = language_v1.LanguageServiceClient()

    # content = 'A happy President Kennedy celebrated at the White House Saturday.'

    if isinstance(content, six.binary_type):
        content = content.decode('utf-8')
    type_ = enums.Document.Type.PLAIN_TEXT
    document = {'content': content, 'type': type_}

    response = client.analyze_entity_sentiment(document)
    for entity in response.entities:
        print('Name: {}'.format(entity.name))
        for mention in entity.mentions:
            print('  - Begin offset : {}'.format(mention.text.begin_offset))
            print('    Content : {}'.format(mention.text.content))
            print('    Magnitude : {}'.format(mention.sentiment.magnitude))
            print('    Sentiment : {}'.format(mention.sentiment.score))
            print('    Type : {}'.format(
                enums.EntityMention.Type(mention.type).name))
        print('  Salience : {}'.format(entity.salience))
        print('  Aggregate sentiment : {}'.format(entity.sentiment.score))
        print('  Aggregate sentiment magnitude : {}'.format(
            entity.sentiment.score))

    # [END language_entity_sentiment_text_core]


# [END language_entity_sentiment_text]


def main():
    import argparse

    parser = argparse.ArgumentParser()
    parser.add_argument(
        '--content',
        type=str,
        default=
        'A happy President Kennedy celebrated at the White House Saturday.')
    args = parser.parse_args()

    sample_analyze_entity_sentiment(args.content)


if __name__ == '__main__':
    main()