google_nlp.ipynb

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   "source": [
    "from google.cloud import language_v1\n",
    "from google.cloud.language_v1 import enums\n",
    "from google.oauth2 import service_account\n",
    "\n",
    "def sample_analyze_entities(text_content):\n",
    "    \"\"\"\n",
    "    Analyzing Entities in a String\n",
    "\n",
    "    Args:\n",
    "      text_content The text content to analyze\n",
    "    \"\"\"\n",
    "    creds = service_account.Credentials.from_service_account_file('Key.json')\n",
    "    client = language_v1.LanguageServiceClient(credentials=creds)\n",
    "\n",
    "    # text_content = 'California is a state.'\n",
    "\n",
    "    # Available types: PLAIN_TEXT, HTML\n",
    "    type_ = enums.Document.Type.PLAIN_TEXT\n",
    "\n",
    "    # Optional. If not specified, the language is automatically detected.\n",
    "    # For list of supported languages:\n",
    "    # https://cloud.google.com/natural-language/docs/languages\n",
    "    language = \"en\"\n",
    "    document = {\"content\": text_content, \"type\": type_, \"language\": language}\n",
    "\n",
    "    # Available values: NONE, UTF8, UTF16, UTF32\n",
    "    encoding_type = enums.EncodingType.UTF8\n",
    "\n",
    "    response = client.analyze_entities(document, encoding_type=encoding_type)\n",
    "    # Loop through entitites returned from the API\n",
    "\n",
    "    \"\"\"\n",
    "    ret = \"\"\n",
    "    for entity in response.entities:\n",
    "        print(u\"Representative name for the entity: {}\".format(entity.name))\n",
    "        ret = (u\"{}\".format(entity.name))\n",
    "        #print(\"ret: \", ret)\n",
    "        \n",
    "        # Get entity type, e.g. PERSON, LOCATION, ADDRESS, NUMBER, et al\n",
    "        print(u\"Entity type: {}\".format(enums.Entity.Type(entity.type).name))\n",
    "        # Get the salience score associated with the entity in the [0, 1.0] range\n",
    "        print(u\"Salience score: {}\".format(entity.salience))\n",
    "        # Loop over the metadata associated with entity. For many known entities,\n",
    "        # the metadata is a Wikipedia URL (wikipedia_url) and Knowledge Graph MID (mid).\n",
    "        # Some entity types may have additional metadata, e.g. ADDRESS entities\n",
    "        # may have metadata for the address street_name, postal_code, et al.\n",
    "        for metadata_name, metadata_value in entity.metadata.items():\n",
    "            print(u\"{}: {}\".format(metadata_name, metadata_value))\n",
    "\n",
    "        # Loop over the mentions of this entity in the input document.\n",
    "        # The API currently supports proper noun mentions.\n",
    "        for mention in entity.mentions:\n",
    "            print(u\"Mention text: {}\".format(mention.text.content))\n",
    "            # Get the mention type, e.g. PROPER for proper noun\n",
    "            print(\n",
    "                u\"Mention type: {}\".format(enums.EntityMention.Type(mention.type).name)\n",
    "            )\n",
    "    \"\"\"\n",
    "\n",
    "    # Get the language of the text, which will be the same as\n",
    "    # the language specified in the request or, if not specified,\n",
    "    # the automatically-detected language.\n",
    "    #print(u\"Language of the text: {}\".format(response.language))\n",
    "    return response.entities[0].name"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#named_entity = sample_analyze_entities(\"obama\")\n",
    "#print(\"entity:\", named_entity)"
   ]
  }
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