El Tiempo de las Historias</time> ... </head> ... <body> ... <p>La vaca saltó 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`_. .. _supported languages: https://cloud.google.com/natural-language/docs/languages In addition to supplying the text / HTML content, a document can refer to content stored in `Google Cloud Storage`_. .. code-block:: python >>> document = language.types.Document( ... gcs_content_uri='gs://my-text-bucket/sentiment-me.txt', ... type=language.enums.HTML, ... ) .. _analyzeEntities: https://cloud.google.com/natural-language/docs/reference/rest/v1/documents/analyzeEntities .. _analyzeSentiment: https://cloud.google.com/natural-language/docs/reference/rest/v1/documents/analyzeSentiment .. _analyzeEntitySentiment: https://cloud.google.com/natural-language/docs/reference/rest/v1/documents/analyzeEntitySentiment .. _annotateText: https://cloud.google.com/natural-language/docs/reference/rest/v1/documents/annotateText .. _classifyText: https://cloud.google.com/natural-language/docs/reference/rest/v1/documents/classifyText .. _Google Cloud Storage: https://cloud.google.com/storage/ Analyze Entities **************** The :meth:`~.language_v1.LanguageServiceClient.analyze_entities` method finds named entities (i.e. proper names) in the text. This method returns a :class:`~.language_v1.types.AnalyzeEntitiesResponse`. .. code-block:: python >>> 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 :meth:`~.language_v1.LanguageServiceClient.analyze_sentiment` method analyzes the sentiment of the provided text. This method returns a :class:`~.language_v1.types.AnalyzeSentimentResponse`. .. code-block:: python >>> 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 .. 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 Entity Sentiment ************************ The :meth:`~.language_v1.LanguageServiceClient.analyze_entity_sentiment` method is effectively the amalgamation of :meth:`~.language_v1.LanguageServiceClient.analyze_entities` and :meth:`~.language_v1.LanguageServiceClient.analyze_sentiment`. This method returns a :class:`~.language_v1.types.AnalyzeEntitySentimentResponse`. .. code-block:: python >>> 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 .. 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``. Annotate Text ************* The :meth:`~.language_v1.LanguageServiceClient.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 :class:`~.language

Using the Language Client ========================= Documents ********* The Google Natural Language API has the following supported methods: - `analyzeEntities`_ - `analyzeSentiment`_ - `analyzeEntitySentiment`_ - `annotateText`_ - `classifyText`_ and each method uses a :class:`~.language_v1.types.Document` for representing text. .. code-block:: python >>> 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: .. code-block:: python >>> html_content = """\ ... ... ... El Tiempo de las Historias</time> ... </head> ... <body> ... <p>La vaca saltó 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`_. .. _supported languages: https://cloud.google.com/natural-language/docs/languages In addition to supplying the text / HTML content, a document can refer to content stored in `Google Cloud Storage`_. .. code-block:: python >>> document = language.types.Document( ... gcs_content_uri='gs://my-text-bucket/sentiment-me.txt', ... type=language.enums.HTML, ... ) .. _analyzeEntities: https://cloud.google.com/natural-language/docs/reference/rest/v1/documents/analyzeEntities .. _analyzeSentiment: https://cloud.google.com/natural-language/docs/reference/rest/v1/documents/analyzeSentiment .. _analyzeEntitySentiment: https://cloud.google.com/natural-language/docs/reference/rest/v1/documents/analyzeEntitySentiment .. _annotateText: https://cloud.google.com/natural-language/docs/reference/rest/v1/documents/annotateText .. _classifyText: https://cloud.google.com/natural-language/docs/reference/rest/v1/documents/classifyText .. _Google Cloud Storage: https://cloud.google.com/storage/ Analyze Entities **************** The :meth:`~.language_v1.LanguageServiceClient.analyze_entities` method finds named entities (i.e. proper names) in the text. This method returns a :class:`~.language_v1.types.AnalyzeEntitiesResponse`. .. code-block:: python >>> 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 :meth:`~.language_v1.LanguageServiceClient.analyze_sentiment` method analyzes the sentiment of the provided text. This method returns a :class:`~.language_v1.types.AnalyzeSentimentResponse`. .. code-block:: python >>> 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 .. 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 Entity Sentiment ************************ The :meth:`~.language_v1.LanguageServiceClient.analyze_entity_sentiment` method is effectively the amalgamation of :meth:`~.language_v1.LanguageServiceClient.analyze_entities` and :meth:`~.language_v1.LanguageServiceClient.analyze_sentiment`. This method returns a :class:`~.language_v1.types.AnalyzeEntitySentimentResponse`. .. code-block:: python >>> 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 .. 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``. Annotate Text ************* The :meth:`~.language_v1.LanguageServiceClient.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 :class:`~.language_v1.types.AnnotateTextResponse`.