Cloud Storage ファイルに対する感情分析

Cloud Storage に保存されているファイルを調べ、テキスト内の感情的傾向を特定します。

このコードサンプルを含む詳細なドキュメントについては、以下をご覧ください。

感情分析

コードサンプル

Go

Natural Language への認証を行うには、アプリケーションのデフォルト認証情報を設定します。詳細については、ローカル開発環境の認証の設定をご覧ください。


func analyzeSentimentFromGCS(ctx context.Context, gcsURI string) (*languagepb.AnalyzeSentimentResponse, error) {
	return client.AnalyzeSentiment(ctx, &languagepb.AnalyzeSentimentRequest{
		Document: &languagepb.Document{
			Source: &languagepb.Document_GcsContentUri{
				GcsContentUri: gcsURI,
			},
			Type: languagepb.Document_PLAIN_TEXT,
		},
	})
}

Java

GitHub で表示フィードバック

// Instantiate the Language client com.google.cloud.language.v1.LanguageServiceClient
try (LanguageServiceClient language = LanguageServiceClient.create()) {
  Document doc =
      Document.newBuilder().setGcsContentUri(gcsUri).setType(Type.PLAIN_TEXT).build();
  AnalyzeSentimentResponse response = language.analyzeSentiment(doc);
  Sentiment sentiment = response.getDocumentSentiment();
  if (sentiment == null) {
    System.out.println("No sentiment found");
  } else {
    System.out.printf("Sentiment magnitude : %.3f\n", sentiment.getMagnitude());
    System.out.printf("Sentiment score : %.3f\n", sentiment.getScore());
  }
  return sentiment;
}

Node.js

GitHub で表示フィードバック

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

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

/**
 * TODO(developer): Uncomment the following lines to run this code
 */
// const bucketName = 'Your bucket name, e.g. my-bucket';
// const fileName = 'Your file name, e.g. my-file.txt';

// Prepares a document, representing a text file in Cloud Storage
const document = {
  gcsContentUri: `gs://${bucketName}/${fileName}`,
  type: 'PLAIN_TEXT',
};

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

const sentiment = result.documentSentiment;
console.log('Document sentiment:');
console.log(`  Score: ${sentiment.score}`);
console.log(`  Magnitude: ${sentiment.magnitude}`);

const sentences = result.sentences;
sentences.forEach(sentence => {
  console.log(`Sentence: ${sentence.text.content}`);
  console.log(`  Score: ${sentence.sentiment.score}`);
  console.log(`  Magnitude: ${sentence.sentiment.magnitude}`);
});

PHP

GitHub で表示フィードバック

use Google\Cloud\Language\V1\Document;
use Google\Cloud\Language\V1\Document\Type;
use Google\Cloud\Language\V1\LanguageServiceClient;

/**
 * @param string $uri The cloud storage object to analyze (gs://your-bucket-name/your-object-name)
 */
function analyze_sentiment_from_file(string $uri): void
{
    $languageServiceClient = new LanguageServiceClient();

    // Create a new Document, pass GCS URI and set type to PLAIN_TEXT
    $document = (new Document())
        ->setGcsContentUri($uri)
        ->setType(Type::PLAIN_TEXT);

    // Call the analyzeSentiment function
    $response = $languageServiceClient->analyzeSentiment($document);
    $document_sentiment = $response->getDocumentSentiment();
    // Print document information
    printf('Document Sentiment:' . PHP_EOL);
    printf('  Magnitude: %s' . PHP_EOL, $document_sentiment->getMagnitude());
    printf('  Score: %s' . PHP_EOL, $document_sentiment->getScore());
    printf(PHP_EOL);
    $sentences = $response->getSentences();
    foreach ($sentences as $sentence) {
        printf('Sentence: %s' . PHP_EOL, $sentence->getText()->getContent());
        printf('Sentence Sentiment:' . PHP_EOL);
        $sentiment = $sentence->getSentiment();
        if ($sentiment) {
            printf('Entity Magnitude: %s' . PHP_EOL, $sentiment->getMagnitude());
            printf('Entity Score: %s' . PHP_EOL, $sentiment->getScore());
        }
        print(PHP_EOL);
    }
}

Python

GitHub で表示フィードバック

from google.cloud import language_v1

def sample_analyze_sentiment(gcs_content_uri):
    """
    Analyzing Sentiment in text file stored in Cloud Storage

    Args:
      gcs_content_uri Google Cloud Storage URI where the file content is located.
      e.g. gs://[Your Bucket]/[Path to File]
    """

    client = language_v1.LanguageServiceClient()

    # gcs_content_uri = 'gs://cloud-samples-data/language/sentiment-positive.txt'

    # Available types: PLAIN_TEXT, HTML
    type_ = language_v1.Document.Type.PLAIN_TEXT

    # Optional. If not specified, the language is automatically detected.
    # For list of supported languages:
    # https://cloud.google.com/natural-language/docs/languages
    language = "en"
    document = {
        "gcs_content_uri": gcs_content_uri,
        "type_": type_,
        "language": language,
    }

    # Available values: NONE, UTF8, UTF16, UTF32
    encoding_type = language_v1.EncodingType.UTF8

    response = client.analyze_sentiment(
        request={"document": document, "encoding_type": encoding_type}
    )
    # Get overall sentiment of the input document
    print(f"Document sentiment score: {response.document_sentiment.score}")
    print(
        f"Document sentiment magnitude: {response.document_sentiment.magnitude}"
    )
    # Get sentiment for all sentences in the document
    for sentence in response.sentences:
        print(f"Sentence text: {sentence.text.content}")
        print(f"Sentence sentiment score: {sentence.sentiment.score}")
        print(f"Sentence sentiment magnitude: {sentence.sentiment.magnitude}")

    # Get the language of the text, which will be the same as
    # the language specified in the request or, if not specified,
    # the automatically-detected language.
    print(f"Language of the text: {response.language}")

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Cloud Storage ファイルに対する感情分析

もっと見る

コードサンプル

Go

Java

Node.js

PHP

Python

次のステップ