Natural Language API「エンティティ分析」の概要と使い方

{
  "entities": [
    {
      "name": "Trump",
      "type": "PERSON",
      "metadata": {
        "mid": "/m/0cqt90",
        "wikipedia_url": "https://en.wikipedia.org/wiki/Donald_Trump"
      },
      "salience": 0.7936003,
      "mentions": [
        {
          "text": {
            "content": "Trump",
            "beginOffset": 10
          },
          "type": "PROPER"
        },
        {
          "text": {
            "content": "President",
            "beginOffset": 0
          },
          "type": "COMMON"
        }
      ]
    },
    {
      "name": "White House",
      "type": "LOCATION",
      "metadata": {
        "mid": "/m/081sq",
        "wikipedia_url": "https://en.wikipedia.org/wiki/White_House"
      },
      "salience": 0.09172433,
      "mentions": [
        {
          "text": {
            "content": "White House",
            "beginOffset": 36
          },
          "type": "PROPER"
        }
      ]
    },
    {
      "name": "Pennsylvania Ave NW",
      "type": "LOCATION",
      "metadata": {
        "mid": "/g/1tgb87cq"
      },
      "salience": 0.085507184,
      "mentions": [
        {
          "text": {
            "content": "Pennsylvania Ave NW",
            "beginOffset": 65
          },
          "type": "PROPER"
        }
      ]
    },
    {
      "name": "Washington, DC",
      "type": "LOCATION",
      "metadata": {
        "mid": "/m/0rh6k",
        "wikipedia_url": "https://en.wikipedia.org/wiki/Washington,_D.C."
      },
      "salience": 0.029168168,
      "mentions": [
        {
          "text": {
            "content": "Washington, DC",
            "beginOffset": 86
          },
          "type": "PROPER"
        }]
    }
    {
      "name": "1600 Pennsylvania Ave NW, Washington, DC",
      "type": "ADDRESS",
      "metadata": {
        "country": "US",
        "sublocality": "Fort Lesley J. McNair",
        "locality": "Washington",
        "street_name": "Pennsylvania Avenue Northwest",
        "broad_region": "District of Columbia",
        "narrow_region": "District of Columbia",
        "street_number": "1600"
      },
      "salience": 0,
      "mentions": [
        {
          "text": {
            "content": "1600 Pennsylvania Ave NW, Washington, DC",
            "beginOffset": 60
          },
          "type": "TYPE_UNKNOWN"
        }]
      }
    }
    {
      "name": "1600",
       "type": "NUMBER",
       "metadata": {
           "value": "1600"
       },
       "salience": 0,
       "mentions": [
         {
          "text": {
              "content": "1600",
              "beginOffset": 60
           },
           "type": "TYPE_UNKNOWN"
        }
     ]
     },
     {
       "name": "October 7",
       "type": "DATE",
       "metadata": {
         "day": "7",
         "month": "10"
       },
       "salience": 0,
       "mentions": [
         {
           "text": {
             "content": "October 7",
             "beginOffset": 105
            },
           "type": "TYPE_UNKNOWN"
         }
       ]
     }
     {
      "name": "7",
      "type": "NUMBER",
      "metadata": {
        "value": "7"
      },
      "salience": 0,
      "mentions": [
        {
          "text": {
            "content": "7",
            "beginOffset": 113
          },
          "type": "TYPE_UNKNOWN"
        }
      ]
    }
  ],
  "language": "en"
}

from google.cloud import language_v1
from google.cloud.language_v1 import enums
from google.cloud.language_v1 import types

def sample_analyze_entities(text_content):
    """
    Analyzing Entities in a String

    Args:
      text_content The text content to analyze
    """

    client = language_v1.LanguageServiceClient()

    type_ = language_v1.types.Document.Type.PLAIN_TEXT

    document = types.Document(
      content=text_content,
      type=enums.Document.Type.PLAIN_TEXT)

    response = client.analyze_entities(document=document)

    # Loop through entitites returned from the API
    for entity in response.entities:
        print(u"Representative name for the entity: {}".format(entity.name))

        # Get entity type, e.g. PERSON, LOCATION, ADDRESS, NUMBER, et al
        print(u"Entity type: {}".format(entity.type))

        # Get the salience score associated with the entity in the [0, 1.0] range
        print(u"Salience score: {}".format(entity.salience))

        # Loop over the metadata associated with entity. For many known entities,
        # the metadata is a Wikipedia URL (wikipedia_url) and Knowledge Graph MID (mid).
        # Some entity types may have additional metadata, e.g. ADDRESS entities
        # may have metadata for the address street_name, postal_code, et al.
        for metadata_name, metadata_value in entity.metadata.items():
            print(u"{}: {}".format(metadata_name, metadata_value))

        # Loop over the mentions of this entity in the input document.
        # The API currently supports proper noun mentions.
        for mention in entity.mentions:
            print(u"Mention text: {}".format(mention.text.content))

            # Get the mention type, e.g. PROPER for proper noun
            print(
                u"Mention type: {}".format(mention.type)
            )

    # the automatically-detected language.
    print(u"Language of the text: {}".format(response.language))

text_content = 'この人は、この世の中で、いちばんしあわせな人にちがいありません。芝居小屋もすばらしいし、お客さんもすばらしい人たちでした。もし中世の時代だったら、おそらく、火あぶりにされたでしょうよ。みんなのうるさいことといったら、まるで、ハエがびんの中で、ブンブンいっているようでした。われわれ人間が、こういうことを考えだすことができるとすれば、われわれは、地の中にうめられるまでに、もっと長生きできてもいいはずだが'

sample_analyze_sentiment(text_content)