Diese Legacy-Version von AutoML Natural Language wurde eingestellt und ist nach dem 31. Dezember 2023 nicht mehr in Google Cloud verfügbar. Alle Funktionen der alten AutoML Natural Language API und neue Features sind auf der Vertex AI-Plattform verfügbar. Informationen zum Migrieren Ihrer Ressourcen finden Sie unter Zu Vertex AI migrieren.

Dataset zur Sentimentanalyse erstellen

Erstellt ein Dataset für die Textsentimentanalyse.

Weitere Informationen

Eine ausführliche Dokumentation, die dieses Codebeispiel enthält, finden Sie hier:

Datasets erstellen und Daten importieren

Codebeispiel

Go

Informationen zum Installieren und Verwenden der Clientbibliothek für AutoML Natural Language finden Sie unter AutoML Natural Language-Clientbibliotheken. Weitere Informationen finden Sie in der Referenzdokumentation zur AutoML Natural Language GoAPI.

Für die Authentifizierung bei AutoML Natural Language richten Sie Standardanmeldedaten für Anwendungen ein. Weitere Informationen finden Sie unter Authentifizierung für eine lokale Entwicklungsumgebung einrichten.

Auf GitHub ansehen

import (
	"context"
	"fmt"
	"io"

	automl "cloud.google.com/go/automl/apiv1"
	"cloud.google.com/go/automl/apiv1/automlpb"
)

// languageSentimentAnalysisCreateDataset creates a dataset for text sentiment analysis.
func languageSentimentAnalysisCreateDataset(w io.Writer, projectID string, location string, datasetName string) error {
	// projectID := "my-project-id"
	// location := "us-central1"
	// datasetName := "dataset_display_name"

	ctx := context.Background()
	client, err := automl.NewClient(ctx)
	if err != nil {
		return fmt.Errorf("NewClient: %w", err)
	}
	defer client.Close()

	req := &automlpb.CreateDatasetRequest{
		Parent: fmt.Sprintf("projects/%s/locations/%s", projectID, location),
		Dataset: &automlpb.Dataset{
			DisplayName: datasetName,
			DatasetMetadata: &automlpb.Dataset_TextSentimentDatasetMetadata{
				TextSentimentDatasetMetadata: &automlpb.TextSentimentDatasetMetadata{
					SentimentMax: 4, // Possible max sentiment score: 1-10
				},
			},
		},
	}

	op, err := client.CreateDataset(ctx, req)
	if err != nil {
		return fmt.Errorf("CreateDataset: %w", err)
	}
	fmt.Fprintf(w, "Processing operation name: %q\n", op.Name())

	dataset, err := op.Wait(ctx)
	if err != nil {
		return fmt.Errorf("Wait: %w", err)
	}

	fmt.Fprintf(w, "Dataset name: %v\n", dataset.GetName())

	return nil
}

Java

Auf GitHub ansehen

import com.google.api.gax.longrunning.OperationFuture;
import com.google.cloud.automl.v1.AutoMlClient;
import com.google.cloud.automl.v1.Dataset;
import com.google.cloud.automl.v1.LocationName;
import com.google.cloud.automl.v1.OperationMetadata;
import com.google.cloud.automl.v1.TextSentimentDatasetMetadata;
import java.io.IOException;
import java.util.concurrent.ExecutionException;

class LanguageSentimentAnalysisCreateDataset {

  static void createDataset() throws IOException, ExecutionException, InterruptedException {
    // TODO(developer): Replace these variables before running the sample.
    String projectId = "YOUR_PROJECT_ID";
    String displayName = "YOUR_DATASET_NAME";
    createDataset(projectId, displayName);
  }

  // Create a dataset
  static void createDataset(String projectId, String displayName)
      throws IOException, ExecutionException, InterruptedException {
    // Initialize client that will be used to send requests. This client only needs to be created
    // once, and can be reused for multiple requests. After completing all of your requests, call
    // the "close" method on the client to safely clean up any remaining background resources.
    try (AutoMlClient client = AutoMlClient.create()) {
      // A resource that represents Google Cloud Platform location.
      LocationName projectLocation = LocationName.of(projectId, "us-central1");
      // Specify the text classification type for the dataset.
      TextSentimentDatasetMetadata metadata =
          TextSentimentDatasetMetadata.newBuilder()
              .setSentimentMax(4) // Possible max sentiment score: 1-10
              .build();
      Dataset dataset =
          Dataset.newBuilder()
              .setDisplayName(displayName)
              .setTextSentimentDatasetMetadata(metadata)
              .build();
      OperationFuture<Dataset, OperationMetadata> future =
          client.createDatasetAsync(projectLocation, dataset);

      Dataset createdDataset = future.get();

      // Display the dataset information.
      System.out.format("Dataset name: %s\n", createdDataset.getName());
      // To get the dataset id, you have to parse it out of the `name` field. As dataset Ids are
      // required for other methods.
      // Name Form: `projects/{project_id}/locations/{location_id}/datasets/{dataset_id}`
      String[] names = createdDataset.getName().split("/");
      String datasetId = names[names.length - 1];
      System.out.format("Dataset id: %s\n", datasetId);
    }
  }
}

Node.js

Auf GitHub ansehen

/**
 * TODO(developer): Uncomment these variables before running the sample.
 */
// const projectId = 'YOUR_PROJECT_ID';
// const location = 'us-central1';
// const displayName = 'YOUR_DISPLAY_NAME';

// Imports the Google Cloud AutoML library
const {AutoMlClient} = require('@google-cloud/automl').v1;

// Instantiates a client
const client = new AutoMlClient();

async function createDataset() {
  // Construct request
  const request = {
    parent: client.locationPath(projectId, location),
    dataset: {
      displayName: displayName,
      textSentimentDatasetMetadata: {
        sentimentMax: 4, // Possible max sentiment score: 1-10
      },
    },
  };

  // Create dataset
  const [operation] = await client.createDataset(request);

  // Wait for operation to complete.
  const [response] = await operation.promise();

  console.log(`Dataset name: ${response.name}`);
  console.log(`
    Dataset id: ${
      response.name
        .split('/')
        [response.name.split('/').length - 1].split('\n')[0]
    }`);
}

createDataset();

Python

Auf GitHub ansehen

from google.cloud import automl

# TODO(developer): Uncomment and set the following variables
# project_id = "YOUR_PROJECT_ID"
# display_name = "YOUR_DATASET_NAME"

client = automl.AutoMlClient()

# A resource that represents Google Cloud Platform location.
project_location = f"projects/{project_id}/locations/us-central1"

# Each dataset requires a sentiment score with a defined sentiment_max
# value, for more information on TextSentimentDatasetMetadata, see:
# https://cloud.google.com/natural-language/automl/docs/prepare#sentiment-analysis
# https://cloud.google.com/automl/docs/reference/rpc/google.cloud.automl.v1#textsentimentdatasetmetadata
metadata = automl.TextSentimentDatasetMetadata(
    sentiment_max=4
)  # Possible max sentiment score: 1-10

dataset = automl.Dataset(
    display_name=display_name, text_sentiment_dataset_metadata=metadata
)

# Create a dataset with the dataset metadata in the region.
response = client.create_dataset(parent=project_location, dataset=dataset)

created_dataset = response.result()

# Display the dataset information
print(f"Dataset name: {created_dataset.name}")
print("Dataset id: {}".format(created_dataset.name.split("/")[-1]))

Nächste Schritte

Informationen zum Suchen und Filtern von Codebeispielen für andere Google Cloud-Produkte finden Sie unter Google Cloud-Beispielbrowser.