使用用戶端程式庫執行情緒分析

本頁面說明如何透過 Google Cloud 用戶端程式庫，以您偏好的程式語言開始使用 Cloud Natural Language API。

事前準備

  1. Sign in to your Google Account.

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

  2. Install the Google Cloud CLI.

  3. 如果您使用外部識別資訊提供者 (IdP)，請先 使用聯合身分登入 gcloud CLI。

  4. 如要初始化 gcloud CLI，請執行下列指令： 

    gcloud init

  5. Create or select a Google Cloud project.

    • Create a Google Cloud project:

      gcloud projects create PROJECT_ID

      Replace PROJECT_ID with a name for the Google Cloud project you are creating.

    • Select the Google Cloud project that you created:

      gcloud config set project PROJECT_ID

      Replace PROJECT_ID with your Google Cloud project name.

  6. Verify that billing is enabled for your Google Cloud project.

  7. Enable the Cloud Natural Language API: 

    gcloud services enable language.googleapis.com

  8. Create local authentication credentials for your user account: 

    gcloud auth application-default login

    If an authentication error is returned, and you are using an external identity provider (IdP), confirm that you have signed in to the gcloud CLI with your federated identity.

  9. Install the Google Cloud CLI.

  10. 如果您使用外部識別資訊提供者 (IdP)，請先 使用聯合身分登入 gcloud CLI。

  11. 如要初始化 gcloud CLI，請執行下列指令： 

    gcloud init

  12. Create or select a Google Cloud project.

    • Create a Google Cloud project:

      gcloud projects create PROJECT_ID

      Replace PROJECT_ID with a name for the Google Cloud project you are creating.

    • Select the Google Cloud project that you created:

      gcloud config set project PROJECT_ID

      Replace PROJECT_ID with your Google Cloud project name.

  13. Verify that billing is enabled for your Google Cloud project.

  14. Enable the Cloud Natural Language API: 

    gcloud services enable language.googleapis.com

  15. Create local authentication credentials for your user account: 

    gcloud auth application-default login

    If an authentication error is returned, and you are using an external identity provider (IdP), confirm that you have signed in to the gcloud CLI with your federated identity.

    16. 安裝用戶端程式庫

    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>26.66.0</version>
      <type>pom</type>
      <scope>import</scope>
    </dependency>
  </dependencies>
</dependencyManagement>

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

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

    implementation 'com.google.cloud:google-cloud-language:2.73.0'

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

    libraryDependencies += "com.google.cloud" % "google-cloud-language" % "2.73.0"

    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

    安裝程式庫前，請確認您已設定適當的 Node.js 開發環境。

    npm install @google-cloud/language

    Python

    安裝程式庫前，請確認您已設定適當的 Python 開發環境。

    pip install --upgrade google-cloud-language

    分析特定文字

    現在您可以使用 Natural Language API 分析特定文字。執行以下程式碼即可進行首次文字情緒分析：

    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

    執行範例前，請確認已設定適當的 Node.js 開發環境。

    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

    執行範例前，請確認已設定適當的 Python 開發環境。

    # 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.types.Document(
    content=text, type_=language_v1.types.Document.Type.PLAIN_TEXT
)

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

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

    恭喜！您已傳送第一個要求到 Natural Language API。

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