Google Cloud Natural Language API Client Library

Common errors

Warnings

Install compatibility verified last tested: 2026-05-12

Imports

• LanguageServiceClient
  wrong

  from google.cloud.language import LanguageServiceClient

  correct

  from google.cloud import language_v1
  client = language_v1.LanguageServiceClient()

  Client classes are typically versioned under `language_v1` or `language_v2` for stability and to distinguish API versions.

• Document.Type
  wrong

  from google.cloud.language import enums

  correct

  from google.cloud import language_v1
  document = language_v1.types.Document(content=text, type_=language_v1.Document.Type.PLAIN_TEXT)

  Enums are now accessed directly via the `language_v1` or `language_v2` module's types, e.g., `language_v1.Document.Type` (note `type_` for the field name to avoid conflict with the Python keyword `type`). The `enums` submodule is deprecated.

Quickstart stale last tested: 2026-04-23

This quickstart demonstrates how to perform sentiment analysis using the `google-cloud-language` library. It initializes the client and sends a text document for sentiment detection. Ensure your Google Cloud project has the Natural Language API enabled and that you've set up authentication, preferably using Application Default Credentials, for local development or deployed applications.

import os
from google.cloud import language_v1

# Set up Application Default Credentials if running locally
# This line is usually not needed when running on Google Cloud services (e.g., Compute Engine, Cloud Run)
# For local development, ensure GOOGLE_APPLICATION_CREDENTIALS points to a service account key file
# or run `gcloud auth application-default login`
# os.environ['GOOGLE_APPLICATION_CREDENTIALS'] = os.environ.get('GOOGLE_APPLICATION_CREDENTIALS', '')

def analyze_sentiment(text_content):
    """Analyzes the sentiment of the provided text."""
    client = language_v1.LanguageServiceClient()

    # Available document types: PLAIN_TEXT, HTML
    document = language_v1.types.Document(
        content=text_content, type_=language_v1.Document.Type.PLAIN_TEXT
    )

    # Detects the sentiment of the document
    sentiment = client.analyze_sentiment(request={'document': document}).document_sentiment

    print(f"Text: {text_content}")
    print(f"Sentiment Score: {sentiment.score}")
    print(f"Sentiment Magnitude: {sentiment.magnitude}")

# Example usage
if __name__ == '__main__':
    analyze_sentiment("Hello, world! This is a great day.")
    analyze_sentiment("I am very unhappy with the service.")