Using the Language Client

Documents

The Google Natural Language API has the following supported methods:

and each method uses a Document for representing text.

>>> document = language.types.Document(
...     content='Google, headquartered in Mountain View, unveiled the '
...             'new Android phone at the Consumer Electronic Show.  '
...             'Sundar Pichai said in his keynote that users love '
...             'their new Android phones.',
...     language='en',
...     type='PLAIN_TEXT',
... )

The document’s language defaults to None, which will cause the API to auto-detect the language.

In addition, you can construct an HTML document:

>>> html_content = """\
... <html>
...   <head>
...     <title>El Tiempo de las Historias</time>
...   </head>
...   <body>
...     <p>La vaca salt&oacute; sobre la luna.</p>
...   </body>
... </html>
... """
>>> document = language.types.Document(
...     content=html_content,
...     language='es',
...     type='HTML',
... )

The language argument can be either ISO-639-1 or BCP-47 language codes. The API reference page contains the full list of supported languages.

In addition to supplying the text / HTML content, a document can refer to content stored in Google Cloud Storage.

>>> document = language.types.Document(
...     gcs_content_uri='gs://my-text-bucket/sentiment-me.txt',
...     type=language.enums.HTML,
... )

Analyze Entities

The analyze_entities() method finds named entities (i.e. proper names) in the text. This method returns a AnalyzeEntitiesResponse.

>>> document = language.types.Document(
...     content='Michelangelo Caravaggio, Italian painter, is '
...             'known for "The Calling of Saint Matthew".',
...     type=language.enums.Document.Type.PLAIN_TEXT,
... )
>>> response = client.analyze_entities(
...     document=document,
...     encoding_type='UTF32',
... )
>>> for entity in response.entities:
...     print('=' * 20)
...     print('         name: {0}'.format(entity.name))
...     print('         type: {0}'.format(entity.type))
...     print('     metadata: {0}'.format(entity.metadata))
...     print('     salience: {0}'.format(entity.salience))
====================
         name: Michelangelo Caravaggio
         type: PERSON
     metadata: {'wikipedia_url': 'https://en.wikipedia.org/wiki/Caravaggio'}
     salience: 0.7615959
====================
         name: Italian
         type: LOCATION
     metadata: {'wikipedia_url': 'https://en.wikipedia.org/wiki/Italy'}
     salience: 0.19960518
====================
         name: The Calling of Saint Matthew
         type: EVENT
     metadata: {'wikipedia_url': 'https://en.wikipedia.org/wiki/The_Calling_of_St_Matthew_(Caravaggio)'}
     salience: 0.038798928

NOTE: It is recommended to send an encoding_type argument to Natural Language methods, so they provide useful offsets for the data they return. While the correct value varies by environment, in Python you usually want UTF32.

Analyze Sentiment

The analyze_sentiment() method analyzes the sentiment of the provided text. This method returns a AnalyzeSentimentResponse.

>>> document = language.types.Document(
...     content='Jogging is not very fun.',
...     type='PLAIN_TEXT',
... )
>>> response = client.analyze_sentiment(
...     document=document,
...     encoding_type='UTF32',
... )
>>> sentiment = response.document_sentiment
>>> print(sentiment.score)
-1
>>> print(sentiment.magnitude)
0.8

Analyze Entity Sentiment

The analyze_entity_sentiment() method is effectively the amalgamation of analyze_entities() and analyze_sentiment(). This method returns a AnalyzeEntitySentimentResponse.

>>> document = language.types.Document(
...     content='Mona said that jogging is very fun.',
...     type='PLAIN_TEXT',
... )
>>> response = client.analyze_entity_sentiment(
...     document=document,
...     encoding_type='UTF32',
... )
>>> entities = response.entities
>>> entities[0].name
'Mona'
>>> entities[1].name
'jogging'
>>> entities[1].sentiment.magnitude
0.8
>>> entities[1].sentiment.score
0.8

Annotate Text

The annotate_text() method analyzes a document and is intended for users who are familiar with machine learning and need in-depth text features to build upon. This method returns a AnnotateTextResponse.