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Perform sentiment analysis by using client libraries

This page shows you how to get started with the Cloud Natural Language API in your favorite programming language using the Google Cloud Client Libraries.

Before you begin

  1. Sign in to your Google Account.

    If you don't already have one, sign up for a new account.

  2. Install and initialize the Google Cloud CLI.
  3. Create or select a Google Cloud project.

    • Create a Cloud project:

      gcloud projects create PROJECT_ID
    • Select the Cloud project that you created:

      gcloud config set project PROJECT_ID
  4. Make sure that billing is enabled for your Cloud project. Learn how to check if billing is enabled on a project.

  5. Enable the Cloud Natural Language API:

    gcloud services enable language.googleapis.com
  6. Create authentication credentials for your Google Account:

    gcloud auth application-default login
  7. Install and initialize the Google Cloud CLI.
  8. Create or select a Google Cloud project.

    • Create a Cloud project:

      gcloud projects create PROJECT_ID
    • Select the Cloud project that you created:

      gcloud config set project PROJECT_ID
  9. Make sure that billing is enabled for your Cloud project. Learn how to check if billing is enabled on a project.

  10. Enable the Cloud Natural Language API:

    gcloud services enable language.googleapis.com
  11. Create authentication credentials for your Google Account:

    gcloud auth application-default login

Install the client library

Go

go get cloud.google.com/go/language/apiv1

Java

If you are using Maven, add the following to your pom.xml file. For more information about BOMs, see The Google Cloud Platform Libraries BOM.

<dependencyManagement>
  <dependencies>
    <dependency>
      <groupId>com.google.cloud</groupId>
      <artifactId>libraries-bom</artifactId>
      <version>26.1.4</version>
      <type>pom</type>
      <scope>import</scope>
    </dependency>
  </dependencies>
</dependencyManagement>

<dependencies>
  <dependency>
    <groupId>com.google.cloud</groupId>
    <artifactId>google-cloud-language</artifactId>
  </dependency>

If you are using Gradle, add the following to your dependencies:

implementation platform('com.google.cloud:libraries-bom:26.1.4')

implementation 'com.google.cloud:google-cloud-language'

If you are using sbt, add the following to your dependencies:

libraryDependencies += "com.google.cloud" % "google-cloud-language" % "2.7.0"

If you're using Visual Studio Code, IntelliJ, or Eclipse, you can add client libraries to your project using the following IDE plugins:

The plugins provide additional functionality, such as key management for service accounts. Refer to each plugin's documentation for details.

Node.js

Before installing the library, make sure you've prepared your environment for Node.js development.

npm install --save @google-cloud/language

Python

Before installing the library, make sure you've prepared your environment for Python development.

pip install --upgrade google-cloud-language

Analyze some text

Now you can use the Natural Language API to analyze some text. Run the following code to perform your first text sentiment analysis:

Go


// Sample language-quickstart uses the Google Cloud Natural API to analyze the
// sentiment of "Hello, world!".
package main

import (
	"context"
	"fmt"
	"log"

	language "cloud.google.com/go/language/apiv1"
	"cloud.google.com/go/language/apiv1/languagepb"
)

func main() {
	ctx := context.Background()

	// Creates a client.
	client, err := language.NewClient(ctx)
	if err != nil {
		log.Fatalf("Failed to create client: %v", err)
	}
	defer client.Close()

	// Sets the text to analyze.
	text := "Hello, world!"

	// Detects the sentiment of the text.
	sentiment, err := client.AnalyzeSentiment(ctx, &languagepb.AnalyzeSentimentRequest{
		Document: &languagepb.Document{
			Source: &languagepb.Document_Content{
				Content: text,
			},
			Type: languagepb.Document_PLAIN_TEXT,
		},
		EncodingType: languagepb.EncodingType_UTF8,
	})
	if err != nil {
		log.Fatalf("Failed to analyze text: %v", err)
	}

	fmt.Printf("Text: %v\n", text)
	if sentiment.DocumentSentiment.Score >= 0 {
		fmt.Println("Sentiment: positive")
	} else {
		fmt.Println("Sentiment: negative")
	}
}

Java

// Imports the Google Cloud client library
import com.google.cloud.language.v1.Document;
import com.google.cloud.language.v1.Document.Type;
import com.google.cloud.language.v1.LanguageServiceClient;
import com.google.cloud.language.v1.Sentiment;

public class QuickstartSample {
  public static void main(String... args) throws Exception {
    // Instantiates a client
    try (LanguageServiceClient language = LanguageServiceClient.create()) {

      // The text to analyze
      String text = "Hello, world!";
      Document doc = Document.newBuilder().setContent(text).setType(Type.PLAIN_TEXT).build();

      // Detects the sentiment of the text
      Sentiment sentiment = language.analyzeSentiment(doc).getDocumentSentiment();

      System.out.printf("Text: %s%n", text);
      System.out.printf("Sentiment: %s, %s%n", sentiment.getScore(), sentiment.getMagnitude());
    }
  }
}

Node.js

Before running the example, make sure you've prepared your environment for Node.js development.

async function quickstart() {
  // Imports the Google Cloud client library
  const language = require('@google-cloud/language');

  // Instantiates a client
  const client = new language.LanguageServiceClient();

  // The text to analyze
  const text = 'Hello, world!';

  const document = {
    content: text,
    type: 'PLAIN_TEXT',
  };

  // Detects the sentiment of the text
  const [result] = await client.analyzeSentiment({document: document});
  const sentiment = result.documentSentiment;

  console.log(`Text: ${text}`);
  console.log(`Sentiment score: ${sentiment.score}`);
  console.log(`Sentiment magnitude: ${sentiment.magnitude}`);
}

Python

Before running the example, make sure you've prepared your environment for Python development.

# Imports the Google Cloud client library
from google.cloud import language_v1

# Instantiates a client
client = language_v1.LanguageServiceClient()

# The text to analyze
text = "Hello, world!"
document = language_v1.Document(
    content=text, type_=language_v1.Document.Type.PLAIN_TEXT
)

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

print("Text: {}".format(text))
print("Sentiment: {}, {}".format(sentiment.score, sentiment.magnitude))

Congratulations! You've sent your first request to the Natural Language API.

How did it go?

Clean up

To avoid incurring charges to your Google Cloud account for the resources used on this page, delete the Cloud project with the resources.

What's next