使用 Amazon Comprehend 示例 AWS SDK for .NET - AWS SDK代码示例

使用 Amazon Comprehend 示例 AWS SDK for .NET

以下代码示例向您展示了如何使用 AWS SDK for .NET 与 Amazon Comprehend 配合使用来执行操作和实现常见场景。

操作是大型程序的代码摘录，必须在上下文中运行。您可以通过操作了解如何调用单个服务函数，还可以通过函数相关场景的上下文查看操作。

场景是向您展示如何通过在一个服务中调用多个函数或与其他 AWS 服务结合来完成特定任务的代码示例。

每个示例都包含一个指向完整源代码的链接，您可以在其中找到有关如何在上下文中设置和运行代码的说明。

主题

操作
场景

操作

以下代码示例显示了如何使用DetectDominantLanguage。

AWS SDK for .NET

注意

还有更多相关信息 GitHub。查找完整示例，学习如何在 AWS 代码示例存储库中进行设置和运行。


    using System;
    using System.Threading.Tasks;
    using Amazon.Comprehend;
    using Amazon.Comprehend.Model;

    /// <summary>
    /// This example calls the Amazon Comprehend service to determine the
    /// dominant language.
    /// </summary>
    public static class DetectDominantLanguage
    {
        /// <summary>
        /// Calls Amazon Comprehend to determine the dominant language used in
        /// the sample text.
        /// </summary>
        public static async Task Main()
        {
            string text = "It is raining today in Seattle.";

            var comprehendClient = new AmazonComprehendClient(Amazon.RegionEndpoint.USWest2);

            Console.WriteLine("Calling DetectDominantLanguage\n");
            var detectDominantLanguageRequest = new DetectDominantLanguageRequest()
            {
                Text = text,
            };

            var detectDominantLanguageResponse = await comprehendClient.DetectDominantLanguageAsync(detectDominantLanguageRequest);
            foreach (var dl in detectDominantLanguageResponse.Languages)
            {
                Console.WriteLine($"Language Code: {dl.LanguageCode}, Score: {dl.Score}");
            }

            Console.WriteLine("Done");
        }
    }

有关API详细信息，请参阅 “AWS SDK for .NET API参考 DetectDominantLanguage” 中的。

以下代码示例显示了如何使用DetectEntities。

AWS SDK for .NET

注意

还有更多相关信息 GitHub。查找完整示例，学习如何在 AWS 代码示例存储库中进行设置和运行。


    using System;
    using System.Threading.Tasks;
    using Amazon.Comprehend;
    using Amazon.Comprehend.Model;

    /// <summary>
    /// This example shows how to use the AmazonComprehend service detect any
    /// entities in submitted text.
    /// </summary>
    public static class DetectEntities
    {
        /// <summary>
        /// The main method calls the DetectEntitiesAsync method to find any
        /// entities in the sample code.
        /// </summary>
        public static async Task Main()
        {
            string text = "It is raining today in Seattle";

            var comprehendClient = new AmazonComprehendClient();

            Console.WriteLine("Calling DetectEntities\n");
            var detectEntitiesRequest = new DetectEntitiesRequest()
            {
                Text = text,
                LanguageCode = "en",
            };
            var detectEntitiesResponse = await comprehendClient.DetectEntitiesAsync(detectEntitiesRequest);

            foreach (var e in detectEntitiesResponse.Entities)
            {
                Console.WriteLine($"Text: {e.Text}, Type: {e.Type}, Score: {e.Score}, BeginOffset: {e.BeginOffset}, EndOffset: {e.EndOffset}");
            }

            Console.WriteLine("Done");
        }
    }

有关API详细信息，请参阅 “AWS SDK for .NET API参考 DetectEntities” 中的。

以下代码示例显示了如何使用DetectKeyPhrases。

AWS SDK for .NET

注意

还有更多相关信息 GitHub。查找完整示例，学习如何在 AWS 代码示例存储库中进行设置和运行。


    using System;
    using System.Threading.Tasks;
    using Amazon.Comprehend;
    using Amazon.Comprehend.Model;

    /// <summary>
    /// This example shows how to use the Amazon Comprehend service to
    /// search text for key phrases.
    /// </summary>
    public static class DetectKeyPhrase
    {
        /// <summary>
        /// This method calls the Amazon Comprehend method DetectKeyPhrasesAsync
        /// to detect any key phrases in the sample text.
        /// </summary>
        public static async Task Main()
        {
            string text = "It is raining today in Seattle";

            var comprehendClient = new AmazonComprehendClient(Amazon.RegionEndpoint.USWest2);

            // Call DetectKeyPhrases API
            Console.WriteLine("Calling DetectKeyPhrases");
            var detectKeyPhrasesRequest = new DetectKeyPhrasesRequest()
            {
                Text = text,
                LanguageCode = "en",
            };
            var detectKeyPhrasesResponse = await comprehendClient.DetectKeyPhrasesAsync(detectKeyPhrasesRequest);
            foreach (var kp in detectKeyPhrasesResponse.KeyPhrases)
            {
                Console.WriteLine($"Text: {kp.Text}, Score: {kp.Score}, BeginOffset: {kp.BeginOffset}, EndOffset: {kp.EndOffset}");
            }

            Console.WriteLine("Done");
        }
    }

有关API详细信息，请参阅 “AWS SDK for .NET API参考 DetectKeyPhrases” 中的。

以下代码示例显示了如何使用DetectPiiEntities。

AWS SDK for .NET

注意

还有更多相关信息 GitHub。查找完整示例，学习如何在 AWS 代码示例存储库中进行设置和运行。


    using System;
    using System.Threading.Tasks;
    using Amazon.Comprehend;
    using Amazon.Comprehend.Model;

    /// <summary>
    /// This example shows how to use the Amazon Comprehend service to find
    /// personally identifiable information (PII) within text submitted to the
    /// DetectPiiEntitiesAsync method.
    /// </summary>
    public class DetectingPII
    {
        /// <summary>
        /// This method calls the DetectPiiEntitiesAsync method to locate any
        /// personally dientifiable information within the supplied text.
        /// </summary>
        public static async Task Main()
        {
            var comprehendClient = new AmazonComprehendClient();
            var text = @"Hello Paul Santos. The latest statement for your
                        credit card account 1111-0000-1111-0000 was
                        mailed to 123 Any Street, Seattle, WA 98109.";

            var request = new DetectPiiEntitiesRequest
            {
                Text = text,
                LanguageCode = "EN",
            };

            var response = await comprehendClient.DetectPiiEntitiesAsync(request);

            if (response.Entities.Count > 0)
            {
                foreach (var entity in response.Entities)
                {
                    var entityValue = text.Substring(entity.BeginOffset, entity.EndOffset - entity.BeginOffset);
                    Console.WriteLine($"{entity.Type}: {entityValue}");
                }
            }
        }
    }

有关API详细信息，请参阅 “AWS SDK for .NET API参考 DetectPiiEntities” 中的。

以下代码示例显示了如何使用DetectSentiment。

AWS SDK for .NET

注意

还有更多相关信息 GitHub。查找完整示例，学习如何在 AWS 代码示例存储库中进行设置和运行。


    using System;
    using System.Threading.Tasks;
    using Amazon.Comprehend;
    using Amazon.Comprehend.Model;

    /// <summary>
    /// This example shows how to detect the overall sentiment of the supplied
    /// text using the Amazon Comprehend service.
    /// </summary>
    public static class DetectSentiment
    {
        /// <summary>
        /// This method calls the DetetectSentimentAsync method to analyze the
        /// supplied text and determine the overal sentiment.
        /// </summary>
        public static async Task Main()
        {
            string text = "It is raining today in Seattle";

            var comprehendClient = new AmazonComprehendClient(Amazon.RegionEndpoint.USWest2);

            // Call DetectKeyPhrases API
            Console.WriteLine("Calling DetectSentiment");
            var detectSentimentRequest = new DetectSentimentRequest()
            {
                Text = text,
                LanguageCode = "en",
            };
            var detectSentimentResponse = await comprehendClient.DetectSentimentAsync(detectSentimentRequest);
            Console.WriteLine($"Sentiment: {detectSentimentResponse.Sentiment}");
            Console.WriteLine("Done");
        }
    }

有关API详细信息，请参阅 “AWS SDK for .NET API参考 DetectSentiment” 中的。

以下代码示例显示了如何使用DetectSyntax。

AWS SDK for .NET

注意

还有更多相关信息 GitHub。查找完整示例，学习如何在 AWS 代码示例存储库中进行设置和运行。


    using System;
    using System.Threading.Tasks;
    using Amazon.Comprehend;
    using Amazon.Comprehend.Model;

    /// <summary>
    /// This example shows how to use Amazon Comprehend to detect syntax
    /// elements by calling the DetectSyntaxAsync method.
    /// </summary>
    public class DetectingSyntax
    {
        /// <summary>
        /// This method calls DetectSynaxAsync to identify the syntax elements
        /// in the sample text.
        /// </summary>
        public static async Task Main()
        {
            string text = "It is raining today in Seattle";

            var comprehendClient = new AmazonComprehendClient();

            // Call DetectSyntax API
            Console.WriteLine("Calling DetectSyntaxAsync\n");
            var detectSyntaxRequest = new DetectSyntaxRequest()
            {
                Text = text,
                LanguageCode = "en",
            };
            DetectSyntaxResponse detectSyntaxResponse = await comprehendClient.DetectSyntaxAsync(detectSyntaxRequest);
            foreach (SyntaxToken s in detectSyntaxResponse.SyntaxTokens)
            {
                Console.WriteLine($"Text: {s.Text}, PartOfSpeech: {s.PartOfSpeech.Tag}, BeginOffset: {s.BeginOffset}, EndOffset: {s.EndOffset}");
            }

            Console.WriteLine("Done");
        }
    }

有关API详细信息，请参阅 “AWS SDK for .NET API参考 DetectSyntax” 中的。

以下代码示例显示了如何使用StartTopicsDetectionJob。

AWS SDK for .NET

注意

还有更多相关信息 GitHub。查找完整示例，学习如何在 AWS 代码示例存储库中进行设置和运行。


    using System;
    using System.Threading.Tasks;
    using Amazon.Comprehend;
    using Amazon.Comprehend.Model;

    /// <summary>
    /// This example scans the documents in an Amazon Simple Storage Service
    /// (Amazon S3) bucket and analyzes it for topics. The results are stored
    /// in another bucket and then the resulting job properties are displayed
    /// on the screen. This example was created using the AWS SDK for .NEt
    /// version 3.7 and .NET Core version 5.0.
    /// </summary>
    public static class TopicModeling
    {
        /// <summary>
        /// This methos calls a topic detection job by calling the Amazon
        /// Comprehend StartTopicsDetectionJobRequest.
        /// </summary>
        public static async Task Main()
        {
            var comprehendClient = new AmazonComprehendClient();

            string inputS3Uri = "s3://input bucket/input path";
            InputFormat inputDocFormat = InputFormat.ONE_DOC_PER_FILE;
            string outputS3Uri = "s3://output bucket/output path";
            string dataAccessRoleArn = "arn:aws:iam::account ID:role/data access role";
            int numberOfTopics = 10;

            var startTopicsDetectionJobRequest = new StartTopicsDetectionJobRequest()
            {
                InputDataConfig = new InputDataConfig()
                {
                    S3Uri = inputS3Uri,
                    InputFormat = inputDocFormat,
                },
                OutputDataConfig = new OutputDataConfig()
                {
                    S3Uri = outputS3Uri,
                },
                DataAccessRoleArn = dataAccessRoleArn,
                NumberOfTopics = numberOfTopics,
            };

            var startTopicsDetectionJobResponse = await comprehendClient.StartTopicsDetectionJobAsync(startTopicsDetectionJobRequest);

            var jobId = startTopicsDetectionJobResponse.JobId;
            Console.WriteLine("JobId: " + jobId);

            var describeTopicsDetectionJobRequest = new DescribeTopicsDetectionJobRequest()
            {
                JobId = jobId,
            };

            var describeTopicsDetectionJobResponse = await comprehendClient.DescribeTopicsDetectionJobAsync(describeTopicsDetectionJobRequest);
            PrintJobProperties(describeTopicsDetectionJobResponse.TopicsDetectionJobProperties);

            var listTopicsDetectionJobsResponse = await comprehendClient.ListTopicsDetectionJobsAsync(new ListTopicsDetectionJobsRequest());
            foreach (var props in listTopicsDetectionJobsResponse.TopicsDetectionJobPropertiesList)
            {
                PrintJobProperties(props);
            }
        }

        /// <summary>
        /// This method is a helper method that displays the job properties
        /// from the call to StartTopicsDetectionJobRequest.
        /// </summary>
        /// <param name="props">A list of properties from the call to
        /// StartTopicsDetectionJobRequest.</param>
        private static void PrintJobProperties(TopicsDetectionJobProperties props)
        {
            Console.WriteLine($"JobId: {props.JobId}, JobName: {props.JobName}, JobStatus: {props.JobStatus}");
            Console.WriteLine($"NumberOfTopics: {props.NumberOfTopics}\nInputS3Uri: {props.InputDataConfig.S3Uri}");
            Console.WriteLine($"InputFormat: {props.InputDataConfig.InputFormat}, OutputS3Uri: {props.OutputDataConfig.S3Uri}");
        }
    }

有关API详细信息，请参阅 “AWS SDK for .NET API参考 StartTopicsDetectionJob” 中的。

场景

以下代码示例说明如何创建应用程序来分析客户意见卡、翻译其母语、确定其情绪并根据译后的文本生成音频文件。

AWS SDK for .NET

此示例应用程序可分析并存储客户反馈卡。具体来说，它满足了纽约市一家虚构酒店的需求。酒店以实体意见卡的形式收集来自不同语种的客人的反馈。该反馈通过 Web 客户端上传到应用程序中。意见卡图片上传后，将执行以下步骤：

使用 Amazon Textract 从图片中提取文本。
Amazon Comprehend 确定所提取文本的情绪及其语言。
使用 Amazon Translate 将所提取文本翻译为英语。
Amazon Polly 根据所提取文本合成音频文件。

完整的应用程序可使用 AWS CDK 进行部署。有关源代码和部署说明，请参阅中的项目 GitHub。

本示例中使用的服务

Amazon Comprehend
Lambda
Amazon Polly
Amazon Textract
Amazon Translate

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