Classify text

Analyze text and return a list of content categories that apply to the text.

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For detailed documentation that includes this code sample, see the following:

Code sample

Go


func classifyText(ctx context.Context, client *language.Client, text string) (*languagepb.ClassifyTextResponse, error) {
	return client.ClassifyText(ctx, &languagepb.ClassifyTextRequest{
		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()) {
  // set content to the text string
  Document doc = Document.newBuilder().setContent(text).setType(Type.PLAIN_TEXT).build();
  ClassifyTextRequest request = ClassifyTextRequest.newBuilder().setDocument(doc).build();
  // detect categories in the given text
  ClassifyTextResponse response = language.classifyText(request);

  for (ClassificationCategory category : response.getCategoriesList()) {
    System.out.printf(
        "Category name : %s, Confidence : %.3f\n",
        category.getName(), category.getConfidence());
  }
}

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',
};

// Classifies text in the document
const [classification] = await client.classifyText({document});
console.log('Categories:');
classification.categories.forEach(category => {
  console.log(`Name: ${category.name}, Confidence: ${category.confidence}`);
});

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 */
// $text = 'The text to analyze.';

// Make sure we have enough words (20+) to call classifyText
if (str_word_count($text) < 20) {
    printf('20+ words are required to classify text.' . PHP_EOL);
    return;
}
$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 analyzeSentiment function
    $response = $languageServiceClient->classifyText($document);
    $categories = $response->getCategories();
    // Print document information
    foreach ($categories as $category) {
        printf('Category Name: %s' . PHP_EOL, $category->getName());
        printf('Confidence: %s' . PHP_EOL, $category->getConfidence());
        print(PHP_EOL);
    }
} finally {
    $languageServiceClient->close();
}

Python

from google.cloud import language_v1

def sample_classify_text(text_content):
    """
    Classifying Content in a String

    Args:
      text_content The text content to analyze. Must include at least 20 words.
    """

    client = language_v1.LanguageServiceClient()

    # text_content = 'That actor on TV makes movies in Hollywood and also stars in a variety of popular new TV shows.'

    # 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}

    response = client.classify_text(request = {'document': document})
    # Loop through classified categories returned from the API
    for category in response.categories:
        # Get the name of the category representing the document.
        # See the predefined taxonomy of categories:
        # https://cloud.google.com/natural-language/docs/categories
        print(u"Category name: {}".format(category.name))
        # Get the confidence. Number representing how certain the classifier
        # is that this category represents the provided text.
        print(u"Confidence: {}".format(category.confidence))

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