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分析 Cloud Storage 文件中的实体情感

确定 Cloud Storage 存储的文件文本中传达的关于实体的情感（积极还是消极）。

深入探索

如需查看包含此代码示例的详细文档，请参阅以下内容：

分析实体情感

代码示例

Java

如需了解如何安装和使用 Natural Language 的客户端库，请参阅 Natural Language 客户端库。如需了解详情，请参阅 Natural Language Java API 参考文档。

如需向 Natural Language 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。

// 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().setGcsContentUri(gcsUri)
          .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 file
  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

如需了解如何安装和使用 Natural Language 的客户端库，请参阅 Natural Language 客户端库。如需了解详情，请参阅 Natural Language Node.js API 参考文档。

如需向 Natural Language 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。

// 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}`);
});

PHP

如需了解如何安装和使用 Natural Language 的客户端库，请参阅 Natural Language 客户端库。

如需向 Natural Language 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。

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 $uri The cloud storage object to analyze (gs://your-bucket-name/your-object-name)
 */
function analyze_entity_sentiment_from_file(string $uri): void
{
    // Create the Natural Language client
    $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 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

如需了解如何安装和使用 Natural Language 的客户端库，请参阅 Natural Language 客户端库。如需了解详情，请参阅 Natural Language Python API 参考文档。

如需向 Natural Language 进行身份验证，请设置应用默认凭据。如需了解详情，请参阅为本地开发环境设置身份验证。

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(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}")

后续步骤

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