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Como analisar o sentimento da entidade

A análise do sentimento da entidade combina ambas as análises de entidade e de sentimento, e tenta determinar o sentimento (positivo ou negativo) manifestado sobre as entidades no texto. O sentimento da entidade é representado por valores de magnitude e pontuação numéricos e é determinado para cada menção de uma entidade. Essas pontuações são então agregadas a uma magnitude e pontuação de sentimento geral de uma entidade. Para mais informações sobre como interpretar os valores de sentimento score e magnitude incluídos na análise, consulte Como interpretar valores da análise de sentimento.

Nos exemplos a seguir, veja como consultar o método analyzeEntitySentiment.

Como analisar o sentimento da entidade

Veja um exemplo de como analisar o sentimento da entidade fornecido como uma string:

Protocolo

Para analisar o sentimento em uma entidade, crie uma solicitação POST para o método REST documents:analyzeEntitySentiment e forneça o corpo da solicitação apropriada, como mostrado no exemplo a seguir.

No exemplo, o comando gcloud auth application-default print-access-token é usado para gerar um token de acesso para uma conta de serviço configurada para o projeto usando o SDK do Cloud do Google Cloud Platform. Para ver instruções sobre como instalar o SDK do Cloud, configurar um projeto com uma conta de serviço, consulte o Guia de início rápido.

curl -X POST \
     -H "Authorization: Bearer "$(gcloud auth application-default print-access-token) \
     -H "Content-Type: application/json; charset=utf-8" \
     --data "{
  'document':{
    'type':'PLAIN_TEXT',
    'content':'I love R&B music. Marvin Gaye is the best.
               \'What\'s Going On\' is one of my favorite songs.
               It was so sad when Marvin Gaye died.'
  },
  'encodingType':'UTF8'
}" "https://language.googleapis.com/v1/documents:analyzeEntitySentiment"

gcloud

Consulte o comando analyze-entity-sentiment para ver todos os detalhes.

Para realizar uma análise de sentimento da entidade, use a ferramenta de linha de comando gcloud e use a sinalização --content para identificar o conteúdo a ser analisado:

gcloud ml language analyze-entity-sentiment \
  --content="I love R&B music. Marvin Gaye is the best. 'What's Going On' is one of my favorite songs. It was so sad when Marvin Gaye died."

C#

private static void AnalyzeEntitySentimentFromText(string text)
{
    var client = LanguageServiceClient.Create();
    var response = client.AnalyzeEntitySentiment(new Document()
    {
        Content = text,
        Type = Document.Types.Type.PlainText
    });
    WriteEntitySentiment(response.Entities);
}

private static void WriteEntitySentiment(IEnumerable<Entity> entities)
{
    Console.WriteLine("Entity Sentiment:");
    foreach (var entity in entities)
    {
        Console.WriteLine($"\t{entity.Name} "
            + $"({(int)(entity.Salience * 100)}%)");
        Console.WriteLine($"\t\tScore: {entity.Sentiment.Score}");
        Console.WriteLine($"\t\tMagnitude { entity.Sentiment.Magnitude}");
    }
}

Go


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

// Instantiate the Language client com.google.cloud.language.v1.LanguageServiceClient
try (LanguageServiceClient language = LanguageServiceClient.create()) {
  Document doc = Document.newBuilder().setContent(text).setType(Type.PLAIN_TEXT).build();
  AnalyzeEntitySentimentRequest request =
      AnalyzeEntitySentimentRequest.newBuilder()
          .setDocument(doc)
          .setEncodingType(EncodingType.UTF16)
          .build();
  // detect entity sentiments in the given string
  AnalyzeEntitySentimentResponse response = language.analyzeEntitySentiment(request);
  // Print the response
  for (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 (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

// 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}`);
});

Python

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.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(u"Representative name for the entity: {}".format(entity.name))
        # Get entity type, e.g. PERSON, LOCATION, ADDRESS, NUMBER, et al
        print(u"Entity type: {}".format(language_v1.Entity.Type(entity.type_).name))
        # Get the salience score associated with the entity in the [0, 1.0] range
        print(u"Salience score: {}".format(entity.salience))
        # Get the aggregate sentiment expressed for this entity in the provided document.
        sentiment = entity.sentiment
        print(u"Entity sentiment score: {}".format(sentiment.score))
        print(u"Entity sentiment magnitude: {}".format(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(u"{} = {}".format(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(u"Mention text: {}".format(mention.text.content))
            # Get the mention type, e.g. PROPER for proper noun
            print(
                u"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(u"Language of the text: {}".format(response.language))

PHP

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

/** Uncomment and populate these variables in your code */
// $text = 'The text to analyze.';

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

Ruby

# text_content = "Text to analyze"

require "google/cloud/language"

language = Google::Cloud::Language.language_service

document = { content: text_content, type: :PLAIN_TEXT }
response = language.analyze_entity_sentiment document: document

response.entities.each do |entity|
  puts "Entity: #{entity.name} Sentiment: #{entity.sentiment.score}"
end

Como analisar o sentimento de entidade do Google Cloud Storage

Veja um exemplo de análise do sentimento da entidade armazenado em um arquivo de texto no Google Cloud Storage:

Protocolo

Para analisar o sentimento da entidade de um documento armazenado no Google Cloud Storage, crie uma solicitação POST para o método REST documents:analyzeEntitySentiment e forneça o caminho para o documento ao corpo da solicitação apropriada, como mostrado no exemplo a seguir.

curl -X POST \
     -H "Authorization: Bearer "$(gcloud auth application-default print-access-token) \
     -H "Content-Type: application/json; charset=utf-8" \
     --data "{
  'document':{
    'type':'PLAIN_TEXT',
    'gcsContentUri':'gs://<bucket-name>/<object-name>'
  }
}" "https://language.googleapis.com/v1/documents:analyzeEntitySentiment"

gcloud

Consulte o comando analyze-entity-sentiment para ver todos os detalhes.

Para realizar uma análise de sentimento da entidade, use a ferramenta de linha de comando gcloud e use a sinalização --content para identificar o conteúdo a ser analisado:

gcloud ml language analyze-entity-sentiment \
  --content-file=gs://<bucket-name>/<object-name>

C#

private static void AnalyzeEntitySentimentFromFile(string gcsUri)
{
    var client = LanguageServiceClient.Create();
    var response = client.AnalyzeEntitySentiment(new Document()
    {
        GcsContentUri = gcsUri,
        Type = Document.Types.Type.PlainText
    });
    WriteEntitySentiment(response.Entities);
}
private static void WriteEntitySentiment(IEnumerable<Entity> entities)
{
    Console.WriteLine("Entity Sentiment:");
    foreach (var entity in entities)
    {
        Console.WriteLine($"\t{entity.Name} "
            + $"({(int)(entity.Salience * 100)}%)");
        Console.WriteLine($"\t\tScore: {entity.Sentiment.Score}");
        Console.WriteLine($"\t\tMagnitude { entity.Sentiment.Magnitude}");
    }
}

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();
  AnalyzeEntitySentimentRequest request =
      AnalyzeEntitySentimentRequest.newBuilder()
          .setDocument(doc)
          .setEncodingType(EncodingType.UTF16)
          .build();
  // Detect entity sentiments in the given file
  AnalyzeEntitySentimentResponse response = language.analyzeEntitySentiment(request);
  // Print the response
  for (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 (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

// 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 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}`);
});

Python

from google.cloud import language_v1

def sample_analyze_entity_sentiment(gcs_content_uri):
    """
    Analyzing Entity 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/entity-sentiment.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_entity_sentiment(request = {'document': document, 'encoding_type': encoding_type})
    # Loop through entitites returned from the API
    for entity in response.entities:
        print(u"Representative name for the entity: {}".format(entity.name))
        # Get entity type, e.g. PERSON, LOCATION, ADDRESS, NUMBER, et al
        print(u"Entity type: {}".format(language_v1.Entity.Type(entity.type_).name))
        # Get the salience score associated with the entity in the [0, 1.0] range
        print(u"Salience score: {}".format(entity.salience))
        # Get the aggregate sentiment expressed for this entity in the provided document.
        sentiment = entity.sentiment
        print(u"Entity sentiment score: {}".format(sentiment.score))
        print(u"Entity sentiment magnitude: {}".format(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(u"{} = {}".format(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(u"Mention text: {}".format(mention.text.content))
            # Get the mention type, e.g. PROPER for proper noun
            print(
                u"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(u"Language of the text: {}".format(response.language))

PHP

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

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

// Create the Natural Language client
$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 analyzeEntitySentiment function
    $response = $languageServiceClient->analyzeEntitySentiment($document);
    $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);
    }
} finally {
    $languageServiceClient->close();
}

Ruby

# storage_path = "Path to file in Google Cloud Storage, eg. gs://bucket/file"

require "google/cloud/language"

language = Google::Cloud::Language.language_service

document = { gcs_content_uri: storage_path, type: :PLAIN_TEXT }
response = language.analyze_entity_sentiment document: document

response.entities.each do |entity|
  puts "Entity: #{entity.name} Sentiment: #{entity.sentiment.score}"
end