分析字符串中的情感

检查给定文本，并识别主要的情感。

代码示例

Go

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

import (
	"context"
	"fmt"
	"io"

	language "cloud.google.com/go/language/apiv2"
	"cloud.google.com/go/language/apiv2/languagepb"
)

// analyzeSentiment sends a string of text to the Cloud Natural Language API to
// assess the sentiment of the text.
func analyzeSentiment(w io.Writer, text string) error {
	ctx := context.Background()

	// Initialize client.
	client, err := language.NewClient(ctx)
	if err != nil {
		return err
	}
	defer client.Close()

	resp, err := client.AnalyzeSentiment(ctx, &languagepb.AnalyzeSentimentRequest{
		Document: &languagepb.Document{
			Source: &languagepb.Document_Content{
				Content: text,
			},
			Type: languagepb.Document_PLAIN_TEXT,
		},
		EncodingType: languagepb.EncodingType_UTF8,
	})

	if err != nil {
		return fmt.Errorf("AnalyzeSentiment: %w", err)
	}
	fmt.Fprintf(w, "Response: %q\n", resp)

	return nil
}

Java

如需了解如何安装和使用 Natural Language 客户端库，请参阅 Natural Language 客户端库。 有关详情，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.v2.LanguageServiceClient
try (LanguageServiceClient language = LanguageServiceClient.create()) {
  Document doc = Document.newBuilder().setContent(text).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

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').v2;

// 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 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

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

/**
 * @param string $text The text to analyze
 */
function analyze_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 analyzeSentiment function
    $request = (new AnalyzeSentimentRequest())
        ->setDocument($document);
    $response = $languageServiceClient->analyzeSentiment($request);
    $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

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_v2


def sample_analyze_sentiment(text_content: str = "I am so happy and joyful.") -> None:
    """
    Analyzes Sentiment in a string.

    Args:
      text_content: The text content to analyze.
    """

    client = language_v2.LanguageServiceClient()

    # text_content = 'I am so happy and joyful.'

    # Available types: PLAIN_TEXT, HTML
    document_type_in_plain_text = language_v2.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_code = "en"
    document = {
        "content": text_content,
        "type_": document_type_in_plain_text,
        "language_code": language_code,
    }

    # Available values: NONE, UTF8, UTF16, UTF32
    # See https://cloud.google.com/natural-language/docs/reference/rest/v2/EncodingType.
    encoding_type = language_v2.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_code}")

后续步骤

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