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Analisar o sentimento da entidade em uma string

Determine o sentimento (positivo ou negativo) manifestado sobre as entidades no texto.

Mais informações

Para ver a documentação detalhada que inclui este exemplo de código, consulte:

Como analisar o sentimento da entidade

Exemplo de código

Go

Para saber como instalar e usar a biblioteca de cliente para a Natural Language, consulte Bibliotecas de cliente da Natural Language. Para mais informações, consulte a documentação de referência da API Natural Language Go.

Para se autenticar no Natural Language, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local.


func analyzeEntitySentiment(ctx context.Context, client *language.Client, text string) (*languagepb.AnalyzeEntitySentimentResponse, error) {
	return client.AnalyzeEntitySentiment(ctx, &languagepb.AnalyzeEntitySentimentRequest{
		Document: &languagepb.Document{
			Source: &languagepb.Document_Content{
				Content: text,
			},
			Type: languagepb.Document_PLAIN_TEXT,
		},
	})
}

Java

Para se autenticar no Natural Language, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local.

// Instantiate the Language client com.google.cloud.language.v1.LanguageServiceClient
try (com.google.cloud.language.v1.LanguageServiceClient language =
    com.google.cloud.language.v1.LanguageServiceClient.create()) {
  com.google.cloud.language.v1.Document doc =
      com.google.cloud.language.v1.Document.newBuilder().setContent(text)
          .setType(com.google.cloud.language.v1.Document.Type.PLAIN_TEXT).build();
  AnalyzeEntitySentimentRequest request =
      AnalyzeEntitySentimentRequest.newBuilder()
          .setDocument(doc)
          .setEncodingType(com.google.cloud.language.v1.EncodingType.UTF16)
          .build();
  // Detect entity sentiments in the given string
  AnalyzeEntitySentimentResponse response = language.analyzeEntitySentiment(request);
  // Print the response
  for (com.google.cloud.language.v1.Entity entity : response.getEntitiesList()) {
    System.out.printf("Entity: %s\n", entity.getName());
    System.out.printf("Salience: %.3f\n", entity.getSalience());
    System.out.printf("Sentiment : %s\n", entity.getSentiment());
    for (com.google.cloud.language.v1.EntityMention mention : entity.getMentionsList()) {
      System.out.printf("Begin offset: %d\n", mention.getText().getBeginOffset());
      System.out.printf("Content: %s\n", mention.getText().getContent());
      System.out.printf("Magnitude: %.3f\n", mention.getSentiment().getMagnitude());
      System.out.printf("Sentiment score : %.3f\n", mention.getSentiment().getScore());
      System.out.printf("Type: %s\n\n", mention.getType());
    }
  }
}

Node.js

Para se autenticar no Natural Language, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local.

// 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 line to run this code.
 */
// const text = 'Your text to analyze, e.g. Hello, world!';

// Prepares a document, representing the provided text
const document = {
  content: text,
  type: 'PLAIN_TEXT',
};

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

console.log('Entities and sentiments:');
entities.forEach(entity => {
  console.log(`  Name: ${entity.name}`);
  console.log(`  Type: ${entity.type}`);
  console.log(`  Score: ${entity.sentiment.score}`);
  console.log(`  Magnitude: ${entity.sentiment.magnitude}`);
});

PHP

Para saber como instalar e usar a biblioteca de cliente para a Natural Language, consulte Bibliotecas de cliente da Natural Language.

Para se autenticar no Natural Language, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local.

use Google\Cloud\Language\V1\AnalyzeEntitySentimentRequest;
use Google\Cloud\Language\V1\Client\LanguageServiceClient;
use Google\Cloud\Language\V1\Document;
use Google\Cloud\Language\V1\Document\Type;
use Google\Cloud\Language\V1\Entity\Type as EntityType;

/**
 * @param string $text The text to analyze
 */
function analyze_entity_sentiment(string $text): void
{
    $languageServiceClient = new LanguageServiceClient();

    // Create a new Document, add text as content and set type to PLAIN_TEXT
    $document = (new Document())
        ->setContent($text)
        ->setType(Type::PLAIN_TEXT);

    // Call the analyzeEntitySentiment function
    $request = (new AnalyzeEntitySentimentRequest())
        ->setDocument($document);
    $response = $languageServiceClient->analyzeEntitySentiment($request);
    $entities = $response->getEntities();
    // Print out information about each entity
    foreach ($entities as $entity) {
        printf('Entity Name: %s' . PHP_EOL, $entity->getName());
        printf('Entity Type: %s' . PHP_EOL, EntityType::name($entity->getType()));
        printf('Entity Salience: %s' . PHP_EOL, $entity->getSalience());
        $sentiment = $entity->getSentiment();
        if ($sentiment) {
            printf('Entity Magnitude: %s' . PHP_EOL, $sentiment->getMagnitude());
            printf('Entity Score: %s' . PHP_EOL, $sentiment->getScore());
        }
        print(PHP_EOL);
    }
}

Python

Para se autenticar no Natural Language, configure o Application Default Credentials. Para mais informações, consulte Configurar a autenticação para um ambiente de desenvolvimento local.

from google.cloud import language_v1


def sample_analyze_entity_sentiment(text_content):
    """
    Analyzing Entity Sentiment in a String

    Args:
      text_content The text content to analyze
    """

    client = language_v1.LanguageServiceClient()

    # text_content = 'Grapes are good. Bananas are bad.'

    # Available types: PLAIN_TEXT, HTML
    type_ = language_v1.types.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 = {"content": text_content, "type_": type_, "language": language}

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

    response = client.analyze_entity_sentiment(
        request={"document": document, "encoding_type": encoding_type}
    )
    # Loop through entitites returned from the API
    for entity in response.entities:
        print(f"Representative name for the entity: {entity.name}")
        # Get entity type, e.g. PERSON, LOCATION, ADDRESS, NUMBER, et al
        print(f"Entity type: {language_v1.Entity.Type(entity.type_).name}")
        # Get the salience score associated with the entity in the [0, 1.0] range
        print(f"Salience score: {entity.salience}")
        # Get the aggregate sentiment expressed for this entity in the provided document.
        sentiment = entity.sentiment
        print(f"Entity sentiment score: {sentiment.score}")
        print(f"Entity sentiment magnitude: {sentiment.magnitude}")
        # Loop over the metadata associated with entity. For many known entities,
        # the metadata is a Wikipedia URL (wikipedia_url) and Knowledge Graph MID (mid).
        # Some entity types may have additional metadata, e.g. ADDRESS entities
        # may have metadata for the address street_name, postal_code, et al.
        for metadata_name, metadata_value in entity.metadata.items():
            print(f"{metadata_name} = {metadata_value}")

        # Loop over the mentions of this entity in the input document.
        # The API currently supports proper noun mentions.
        for mention in entity.mentions:
            print(f"Mention text: {mention.text.content}")
            # Get the mention type, e.g. PROPER for proper noun
            print(
                "Mention type: {}".format(
                    language_v1.EntityMention.Type(mention.type_).name
                )
            )

    # 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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