클라이언트 라이브러리를 사용한 감정 분석 수행

이 페이지에서는 Google Cloud 클라이언트 라이브러리를 사용하여 선호하는 프로그래밍 언어로 Cloud Natural Language API를 시작하는 방법을 보여줍니다.

시작하기 전에

  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. To initialize the gcloud CLI, run the following command: 

    gcloud init

  4. 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.

  5. Make sure that billing is enabled for your Google Cloud project.

  6. Enable the Cloud Natural Language API: 

    gcloud services enable language.googleapis.com

  7. Create local authentication credentials for your user account: 

    gcloud auth application-default login
  8. Install the Google Cloud CLI.

  9. To initialize the gcloud CLI, run the following command: 

    gcloud init

  10. 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.

  11. Make sure that billing is enabled for your Google Cloud project.

  12. Enable the Cloud Natural Language API: 

    gcloud services enable language.googleapis.com

  13. Create local authentication credentials for your user account: 

    gcloud auth application-default login

클라이언트 라이브러리 설치

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>26.39.0</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 'com.google.cloud:google-cloud-language:2.45.0'

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

libraryDependencies += "com.google.cloud" % "google-cloud-language" % "2.45.0"

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

라이브러리를 설치하기 전에 Node.js 개발을 위한 환경이 준비됐는지 확인하세요.

npm install --save @google-cloud/language

Python

라이브러리를 설치하기 전에 Python 개발을 위한 환경이 준비됐는지 확인하세요.

pip install --upgrade google-cloud-language

일부 텍스트 분석

이제 Natural Language API를 사용하여 일부 텍스트를 분석할 수 있습니다. 텍스트 감정 분석을 처음 수행하려면 다음 코드를 실행합니다.

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

예시를 실행하기 전에 Node.js 개발 환경이 준비됐는지 확인합니다.

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

예시를 실행하기 전에 Python 개발 환경이 준비됐는지 확인합니다.

# 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}")

수고하셨습니다. 처음으로 요청을 Natural Language API에 보냈습니다.

어땠나요?

삭제

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