Analyser les sentiments dans un fichier Cloud Storage

Inspecte un fichier stocké dans Cloud Storage et identifie l'opinion émotionnelle dominante dans le texte.

Pages de documentation incluant cet exemple de code

Pour afficher l'exemple de code utilisé en contexte, consultez la documentation suivante :

Exemple de code

Go


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

// 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

// 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

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

/** Uncomment and populate these variables in your code */
// $uri = 'The cloud storage object to analyze (gs://your-bucket-name/your-object-name)';

$languageServiceClient = new LanguageServiceClient();
try {
    // 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);
    }
} finally {
    $languageServiceClient->close();
}

Python

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(u"Document sentiment score: {}".format(response.document_sentiment.score))
    print(
        u"Document sentiment magnitude: {}".format(
            response.document_sentiment.magnitude
        )
    )
    # Get sentiment for all sentences in the document
    for sentence in response.sentences:
        print(u"Sentence text: {}".format(sentence.text.content))
        print(u"Sentence sentiment score: {}".format(sentence.sentiment.score))
        print(u"Sentence sentiment magnitude: {}".format(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(u"Language of the text: {}".format(response.language))

Étape suivante

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