Schnellstart: Verwenden der Textanalyse-Clientbibliothek und der REST-APIQuickstart: Use the Text Analytics client library and REST API

v3.1-Referenzdokumentation | v3.1-Bibliotheksquellcode | v3.1-Paket (NuGet) | v3.1-Beispielev3.1 Reference documentation | v3.1 Library source code | v3.1 Package (NuGet) | v3.1 Samples

v3-Referenzdokumentation | Quellcode der v3-Bibliothek | v3-Paket (NuGet) | v3-Beispielev3 Reference documentation | v3 Library source code | v3 Package (NuGet) | v3 Samples

v2-Referenzdokumentation | Quellcode der v2-Bibliothek | v2-Paket (NuGet) | v2-Beispielev2 Reference documentation | v2 Library source code | v2 Package (NuGet) | v2 Samples

Installieren Sie die Clientbibliothek, indem Sie im Projektmappen-Explorer mit der rechten Maustaste auf die Projektmappe klicken und NuGet-Pakete verwalten auswählen.Install the client library by right-clicking on the solution in the Solution Explorer and selecting Manage NuGet Packages. Wählen Sie im daraufhin geöffneten Paket-Manager die Option Durchsuchen aus, und suchen Sie nach Azure.AI.TextAnalytics.In the package manager that opens select Browse and search for Azure.AI.TextAnalytics. Aktivieren Sie das Kontrollkästchen include prerelase (Vorabversionen einbeziehen), wählen Sie die Version 5.1.0-beta.3 aus, und installieren Sie.Check the include prerelase box, select version 5.1.0-beta.3, and then Install. Sie können auch die Paket-Manager-Konsole verwenden.You can also use the Package Manager Console.

Installieren Sie die Clientbibliothek, indem Sie im Projektmappen-Explorer mit der rechten Maustaste auf die Projektmappe klicken und NuGet-Pakete verwalten auswählen.Install the client library by right-clicking on the solution in the Solution Explorer and selecting Manage NuGet Packages. Wählen Sie im daraufhin geöffneten Paket-Manager die Option Durchsuchen aus, und suchen Sie nach Azure.AI.TextAnalytics.In the package manager that opens select Browse and search for Azure.AI.TextAnalytics. Wählen Sie die Version 5.0.0 und anschließend Installieren aus.Select version 5.0.0, and then Install. Sie können auch die Paket-Manager-Konsole verwenden.You can also use the Package Manager Console.

Installieren Sie die Clientbibliothek, indem Sie im Projektmappen-Explorer mit der rechten Maustaste auf die Projektmappe klicken und NuGet-Pakete verwalten auswählen.Install the client library by right-clicking on the solution in the Solution Explorer and selecting Manage NuGet Packages. Wählen Sie im daraufhin geöffneten Paket-Manager die Option Durchsuchen aus, und suchen Sie nach Microsoft.Azure.CognitiveServices.Language.TextAnalytics.In the package manager that opens, select Browse and search for Microsoft.Azure.CognitiveServices.Language.TextAnalytics. Klicken Sie darauf, und klicken Sie dann auf Installieren.Click on it, and then Install. Sie können auch die Paket-Manager-Konsole verwenden.You can also use the Package Manager Console.

Öffnen Sie die Datei program.cs, und fügen Sie die folgenden using-Anweisungen hinzu:Open the program.cs file and add the following using directives:

using Azure;
using System;
using System.Globalization;
using Azure.AI.TextAnalytics;

Erstellen Sie in der Program-Klasse der Anwendung Variablen für den Schlüssel und Endpunkt Ihrer Ressource.In the application's Program class, create variables for your resource's key and endpoint.

private static readonly AzureKeyCredential credentials = new AzureKeyCredential("<replace-with-your-text-analytics-key-here>");
private static readonly Uri endpoint = new Uri("<replace-with-your-text-analytics-endpoint-here>");

Ersetzen Sie die Main-Methode der Anwendung.Replace the application's Main method. Die hier aufgerufenen Methoden definieren Sie später.You will define the methods called here later.

static void Main(string[] args)
{
    var client = new TextAnalyticsClient(endpoint, credentials);
    // You will implement these methods later in the quickstart.
    SentimentAnalysisExample(client);
    SentimentAnalysisWithOpinionMiningExample(client);
    LanguageDetectionExample(client);
    EntityRecognitionExample(client);
    EntityLinkingExample(client);
    RecognizePIIExample(client);
    KeyPhraseExtractionExample(client);

    Console.Write("Press any key to exit.");
    Console.ReadKey();
}

Öffnen Sie die Datei program.cs, und fügen Sie die folgenden using-Anweisungen hinzu:Open the program.cs file and add the following using directives:

using Azure;
using System;
using System.Globalization;
using Azure.AI.TextAnalytics;

Erstellen Sie in der Program-Klasse der Anwendung Variablen für den Schlüssel und Endpunkt Ihrer Ressource.In the application's Program class, create variables for your resource's key and endpoint.

private static readonly AzureKeyCredential credentials = new AzureKeyCredential("<replace-with-your-text-analytics-key-here>");
private static readonly Uri endpoint = new Uri("<replace-with-your-text-analytics-endpoint-here>");

Ersetzen Sie die Main-Methode der Anwendung.Replace the application's Main method. Die hier aufgerufenen Methoden definieren Sie später.You will define the methods called here later.

static void Main(string[] args)
{
    var client = new TextAnalyticsClient(endpoint, credentials);
    // You will implement these methods later in the quickstart.
    SentimentAnalysisExample(client);
    LanguageDetectionExample(client);
    EntityRecognitionExample(client);
    EntityLinkingExample(client);
    KeyPhraseExtractionExample(client);

    Console.Write("Press any key to exit.");
    Console.ReadKey();
}

Öffnen Sie die Datei program.cs, und fügen Sie die folgenden using-Anweisungen hinzu:Open the program.cs file and add the following using directives:

using System;
using System.Collections.Generic;
using System.Net.Http;
using System.Threading;
using System.Threading.Tasks;
using Microsoft.Azure.CognitiveServices.Language.TextAnalytics;
using Microsoft.Azure.CognitiveServices.Language.TextAnalytics.Models;
using Microsoft.Rest;

Erstellen Sie in der Program-Klasse der Anwendung Variablen für den Schlüssel und Endpunkt Ihrer Ressource.In the application's Program class, create variables for your resource's key and endpoint.

private static readonly string key = "<replace-with-your-text-analytics-key-here>";
private static readonly string endpoint = "<replace-with-your-text-analytics-endpoint-here>";

Ersetzen Sie die Main-Methode der Anwendung.Replace the application's Main method. Die hier aufgerufenen Methoden definieren Sie später.You will define the methods called here later.

static void Main(string[] args)
{
    var client = authenticateClient();

    sentimentAnalysisExample(client);
    languageDetectionExample(client);
    entityRecognitionExample(client);
    keyPhraseExtractionExample(client);
    Console.Write("Press any key to exit.");
    Console.ReadKey();
}

Stellen Sie sicher, dass mit Ihrer zuvor erstellten Main-Methode ein neues Clientobjekt mit Ihrem Endpunkt und Ihren Anmeldeinformationen erstellt wird.Make sure your main method from earlier creates a new client object with your endpoint and credentials.

var client = new TextAnalyticsClient(endpoint, credentials);

Stellen Sie sicher, dass mit Ihrer zuvor erstellten Main-Methode ein neues Clientobjekt mit Ihrem Endpunkt und Ihren Anmeldeinformationen erstellt wird.Make sure your main method from earlier creates a new client object with your endpoint and credentials.

var client = new TextAnalyticsClient(endpoint, credentials);

Erstellen Sie eine neue Klasse vom Typ ApiKeyServiceClientCredentials, um die Anmeldeinformationen zu speichern und den Anforderungen des Clients hinzuzufügen.Create a new ApiKeyServiceClientCredentials class to store the credentials and add them to the client's requests. Erstellen Sie darin eine Außerkraftsetzung für ProcessHttpRequestAsync(), um Ihren Schlüssel dem Header Ocp-Apim-Subscription-Key hinzuzufügen.Within it, create an override for ProcessHttpRequestAsync() that adds your key to the Ocp-Apim-Subscription-Key header.

class ApiKeyServiceClientCredentials : ServiceClientCredentials
{
    private readonly string apiKey;

    public ApiKeyServiceClientCredentials(string apiKey)
    {
        this.apiKey = apiKey;
    }

    public override Task ProcessHttpRequestAsync(HttpRequestMessage request, CancellationToken cancellationToken)
    {
        if (request == null)
        {
            throw new ArgumentNullException("request");
        }
        request.Headers.Add("Ocp-Apim-Subscription-Key", this.apiKey);
        return base.ProcessHttpRequestAsync(request, cancellationToken);
    }
}

Erstellen Sie eine Methode, um das TextAnalyticsClient-Objekt mit Ihrem Endpunkt und einem ApiKeyServiceClientCredentials-Objekt zu instanziieren, das Ihren Schlüssel enthält.Create a method to instantiate the TextAnalyticsClient object with your endpoint and a ApiKeyServiceClientCredentials object containing your key.

static TextAnalyticsClient authenticateClient()
{
    ApiKeyServiceClientCredentials credentials = new ApiKeyServiceClientCredentials(key);
    TextAnalyticsClient client = new TextAnalyticsClient(credentials)
    {
        Endpoint = endpoint
    };
    return client;
}

Erstellen Sie eine neue Funktion namens SentimentAnalysisExample(), die den zuvor erstellten Client verwendet, und rufen Sie die zugehörige Funktion AnalyzeSentiment() auf.Create a new function called SentimentAnalysisExample() that takes the client that you created earlier, and call its AnalyzeSentiment() function. Das zurückgegebene Objekt vom Typ Response<DocumentSentiment> enthält die Stimmungsbezeichnung und den Stimmungswert des gesamten Eingabedokuments sowie eine Standpunktanalyse für jeden Satz (sofern erfolgreich).The returned Response<DocumentSentiment> object will contain the sentiment label and score of the entire input document, as well as a sentiment analysis for each sentence if successful. Im Falle eines Fehlers wird ein Fehler vom Typ RequestFailedException ausgelöst.If there was an error, it will throw a RequestFailedException.

static void SentimentAnalysisExample(TextAnalyticsClient client)
{
    string inputText = "I had the best day of my life. I wish you were there with me.";
    DocumentSentiment documentSentiment = client.AnalyzeSentiment(inputText);
    Console.WriteLine($"Document sentiment: {documentSentiment.Sentiment}\n");

    foreach (var sentence in documentSentiment.Sentences)
    {
        Console.WriteLine($"\tText: \"{sentence.Text}\"");
        Console.WriteLine($"\tSentence sentiment: {sentence.Sentiment}");
        Console.WriteLine($"\tPositive score: {sentence.ConfidenceScores.Positive:0.00}");
        Console.WriteLine($"\tNegative score: {sentence.ConfidenceScores.Negative:0.00}");
        Console.WriteLine($"\tNeutral score: {sentence.ConfidenceScores.Neutral:0.00}\n");
    }
}

OutputOutput

Document sentiment: Positive

        Text: "I had the best day of my life."
        Sentence sentiment: Positive
        Positive score: 1.00
        Negative score: 0.00
        Neutral score: 0.00

        Text: "I wish you were there with me."
        Sentence sentiment: Neutral
        Positive score: 0.21
        Negative score: 0.02
        Neutral score: 0.77

Opinion MiningOpinion mining

Erstellen Sie eine neue Funktion namens SentimentAnalysisWithOpinionMiningExample(), die den zuvor erstellten Client verwendet, und rufen Sie die zugehörige Funktion AnalyzeSentimentBatch() mit der Option AdditionalSentimentAnalyses.OpinionMining auf.Create a new function called SentimentAnalysisWithOpinionMiningExample() that takes the client that you created earlier, and call its AnalyzeSentimentBatch() function with AdditionalSentimentAnalyses.OpinionMining option. Das zurückgegebene AnalyzeSentimentResultCollection-Objekt enthält die Auflistung von AnalyzeSentimentResult, in der Response<DocumentSentiment> darstellt ist.The returned AnalyzeSentimentResultCollection object will contain the collection of AnalyzeSentimentResult in which represents Response<DocumentSentiment>. Der Unterschied zwischen SentimentAnalysis() und SentimentAnalysisWithOpinionMiningExample() besteht darin, dass das letztere in jedem Satz MinedOpinion enthält, was einen analysierten Aspekt und die zugehörige(n) Meinung(en) anzeigt.The difference between SentimentAnalysis() and SentimentAnalysisWithOpinionMiningExample() is that the latter will contain MinedOpinion in each sentence, which shows an analyzed aspect and the related opinion(s). Im Falle eines Fehlers wird ein Fehler vom Typ RequestFailedException ausgelöst.If there was an error, it will throw a RequestFailedException.

static void SentimentAnalysisWithOpinionMiningExample(TextAnalyticsClient client)
{
    var documents = new List<string>
    {
        "The food and service were unacceptable, but the concierge were nice."
    };

    AnalyzeSentimentResultCollection reviews = client.AnalyzeSentimentBatch(documents, options: new AnalyzeSentimentOptions()
    {
        IncludeOpinionMining = true
    });

    foreach (AnalyzeSentimentResult review in reviews)
    {
        Console.WriteLine($"Document sentiment: {review.DocumentSentiment.Sentiment}\n");
        Console.WriteLine($"\tPositive score: {review.DocumentSentiment.ConfidenceScores.Positive:0.00}");
        Console.WriteLine($"\tNegative score: {review.DocumentSentiment.ConfidenceScores.Negative:0.00}");
        Console.WriteLine($"\tNeutral score: {review.DocumentSentiment.ConfidenceScores.Neutral:0.00}\n");
        foreach (SentenceSentiment sentence in review.DocumentSentiment.Sentences)
        {
            Console.WriteLine($"\tText: \"{sentence.Text}\"");
            Console.WriteLine($"\tSentence sentiment: {sentence.Sentiment}");
            Console.WriteLine($"\tSentence positive score: {sentence.ConfidenceScores.Positive:0.00}");
            Console.WriteLine($"\tSentence negative score: {sentence.ConfidenceScores.Negative:0.00}");
            Console.WriteLine($"\tSentence neutral score: {sentence.ConfidenceScores.Neutral:0.00}\n");

            foreach (MinedOpinion minedOpinion in sentence.MinedOpinions)
            {
                Console.WriteLine($"\tAspect: {minedOpinion.Aspect.Text}, Value: {minedOpinion.Aspect.Sentiment}");
                Console.WriteLine($"\tAspect positive score: {minedOpinion.Aspect.ConfidenceScores.Positive:0.00}");
                Console.WriteLine($"\tAspect negative score: {minedOpinion.Aspect.ConfidenceScores.Negative:0.00}");
                foreach (OpinionSentiment opinion in minedOpinion.Opinions)
                {
                    Console.WriteLine($"\t\tRelated Opinion: {opinion.Text}, Value: {opinion.Sentiment}");
                    Console.WriteLine($"\t\tRelated Opinion positive score: {opinion.ConfidenceScores.Positive:0.00}");
                    Console.WriteLine($"\t\tRelated Opinion negative score: {opinion.ConfidenceScores.Negative:0.00}");
                }
            }
        }
        Console.WriteLine($"\n");
    }
}

OutputOutput

Document sentiment: Positive

        Positive score: 0.84
        Negative score: 0.16
        Neutral score: 0.00

        Text: "The food and service were unacceptable, but the concierge were nice."
        Sentence sentiment: Positive
        Sentence positive score: 0.84
        Sentence negative score: 0.16
        Sentence neutral score: 0.00

        Aspect: food, Value: Negative
        Aspect positive score: 0.01
        Aspect negative score: 0.99
                Related Opinion: unacceptable, Value: Negative
                Related Opinion positive score: 0.01
                Related Opinion negative score: 0.99
        Aspect: service, Value: Negative
        Aspect positive score: 0.01
        Aspect negative score: 0.99
                Related Opinion: unacceptable, Value: Negative
                Related Opinion positive score: 0.01
                Related Opinion negative score: 0.99
        Aspect: concierge, Value: Positive
        Aspect positive score: 1.00
        Aspect negative score: 0.00
                Related Opinion: nice, Value: Positive
                Related Opinion positive score: 1.00
                Related Opinion negative score: 0.00


Press any key to exit.

Erstellen Sie eine neue Funktion namens SentimentAnalysisExample(), die den zuvor erstellten Client verwendet, und rufen Sie die zugehörige Funktion AnalyzeSentiment() auf.Create a new function called SentimentAnalysisExample() that takes the client that you created earlier, and call its AnalyzeSentiment() function. Das zurückgegebene Objekt vom Typ Response<DocumentSentiment> enthält die Stimmungsbezeichnung und den Stimmungswert des gesamten Eingabedokuments sowie eine Standpunktanalyse für jeden Satz (sofern erfolgreich).The returned Response<DocumentSentiment> object will contain the sentiment label and score of the entire input document, as well as a sentiment analysis for each sentence if successful. Im Falle eines Fehlers wird ein Fehler vom Typ RequestFailedException ausgelöst.If there was an error, it will throw a RequestFailedException.

static void SentimentAnalysisExample(TextAnalyticsClient client)
{
    string inputText = "I had the best day of my life. I wish you were there with me.";
    DocumentSentiment documentSentiment = client.AnalyzeSentiment(inputText);
    Console.WriteLine($"Document sentiment: {documentSentiment.Sentiment}\n");

    foreach (var sentence in documentSentiment.Sentences)
    {
        Console.WriteLine($"\tText: \"{sentence.Text}\"");
        Console.WriteLine($"\tSentence sentiment: {sentence.Sentiment}");
        Console.WriteLine($"\tPositive score: {sentence.ConfidenceScores.Positive:0.00}");
        Console.WriteLine($"\tNegative score: {sentence.ConfidenceScores.Negative:0.00}");
        Console.WriteLine($"\tNeutral score: {sentence.ConfidenceScores.Neutral:0.00}\n");
    }
}

OutputOutput

Document sentiment: Positive

        Text: "I had the best day of my life."
        Sentence sentiment: Positive
        Positive score: 1.00
        Negative score: 0.00
        Neutral score: 0.00

        Text: "I wish you were there with me."
        Sentence sentiment: Neutral
        Positive score: 0.21
        Negative score: 0.02
        Neutral score: 0.77

Erstellen Sie eine neue Funktion namens SentimentAnalysisExample(), die den zuvor erstellten Client verwendet, und rufen Sie die zugehörige Funktion Sentiment() auf.Create a new function called SentimentAnalysisExample() that takes the client that you created earlier, and call its Sentiment() function. Das zurückgegebene Objekt vom Typ SentimentResult enthält bei erfolgreicher Ausführung die Stimmung Score und andernfalls errorMessage.The returned SentimentResult object will contain the sentiment Score if successful, and an errorMessage if not.

Eine gegen 0 tendierende Punktzahl deutet auf eine negative Stimmung hin, eine gegen 1 tendierende Punktzahl auf eine positive.A score that's close to 0 indicates a negative sentiment, while a score that's closer to 1 indicates a positive sentiment.

static void sentimentAnalysisExample(ITextAnalyticsClient client)
{
    var result = client.Sentiment("I had the best day of my life.", "en");
    Console.WriteLine($"Sentiment Score: {result.Score:0.00}");
}

Sentiment Score: 0.87

Erstellen Sie eine neue Funktion namens LanguageDetectionExample(), die den zuvor erstellten Client verwendet, und rufen Sie die zugehörige Funktion DetectLanguage() auf.Create a new function called LanguageDetectionExample() that takes the client that you created earlier, and call its DetectLanguage() function. Das zurückgegebene Objekt vom Typ Response<DetectedLanguage> enthält die erkannte Sprache sowie den zugehörigen Namen und ISO-6391-Code.The returned Response<DetectedLanguage> object will contain the detected language along with its name and ISO-6391 code. Im Falle eines Fehlers wird ein Fehler vom Typ RequestFailedException ausgelöst.If there was an error, it will throw a RequestFailedException.

static void LanguageDetectionExample(TextAnalyticsClient client)
{
    DetectedLanguage detectedLanguage = client.DetectLanguage("Ce document est rédigé en Français.");
    Console.WriteLine("Language:");
    Console.WriteLine($"\t{detectedLanguage.Name},\tISO-6391: {detectedLanguage.Iso6391Name}\n");
}

OutputOutput

Language:
        French, ISO-6391: fr

Erstellen Sie eine neue Funktion namens LanguageDetectionExample(), die den zuvor erstellten Client verwendet, und rufen Sie die zugehörige Funktion DetectLanguage() auf.Create a new function called LanguageDetectionExample() that takes the client that you created earlier, and call its DetectLanguage() function. Das zurückgegebene Objekt vom Typ Response<DetectedLanguage> enthält die erkannte Sprache sowie den zugehörigen Namen und ISO-6391-Code.The returned Response<DetectedLanguage> object will contain the detected language along with its name and ISO-6391 code. Im Falle eines Fehlers wird ein Fehler vom Typ RequestFailedException ausgelöst.If there was an error, it will throw a RequestFailedException.

static void LanguageDetectionExample(TextAnalyticsClient client)
{
    DetectedLanguage detectedLanguage = client.DetectLanguage("Ce document est rédigé en Français.");
    Console.WriteLine("Language:");
    Console.WriteLine($"\t{detectedLanguage.Name},\tISO-6391: {detectedLanguage.Iso6391Name}\n");
}

OutputOutput

Language:
        French, ISO-6391: fr

Erstellen Sie eine neue Funktion namens languageDetectionExample(), die den zuvor erstellten Client verwendet, und rufen Sie die zugehörige Funktion DetectLanguage() auf.Create a new function called languageDetectionExample() that takes the client that you created earlier, and call its DetectLanguage() function. Das zurückgegebene Objekt vom Typ LanguageResult enthält bei erfolgreicher Ausführung die Liste der erkannten Sprachen in DetectedLanguages und andernfalls errorMessage.The returned LanguageResult object will contain the list of detected languages in DetectedLanguages if successful, and an errorMessage if not. Geben Sie die erste zurückgegebene Sprache aus.Print the first returned language.

static void languageDetectionExample(ITextAnalyticsClient client)
{
    var result = client.DetectLanguage("This is a document written in English.");
    Console.WriteLine($"Language: {result.DetectedLanguages[0].Name}");
}

OutputOutput

Language: English

Erstellen Sie eine neue Funktion namens EntityRecognitionExample(), die den zuvor erstellten Client verwendet, rufen Sie die zugehörige Funktion RecognizeEntities() auf, und durchlaufen Sie die Ergebnisse.Create a new function called EntityRecognitionExample() that takes the client that you created earlier, call its RecognizeEntities() function and iterate through the results. Das zurückgegebene Response<CategorizedEntityCollection>-Objekt enthält die Sammlung der erkannten CategorizedEntity-Entitäten.The returned Response<CategorizedEntityCollection> object will contain the collection of detected entities CategorizedEntity. Im Falle eines Fehlers wird ein Fehler vom Typ RequestFailedException ausgelöst.If there was an error, it will throw a RequestFailedException.

static void EntityRecognitionExample(TextAnalyticsClient client)
{
    var response = client.RecognizeEntities("I had a wonderful trip to Seattle last week.");
    Console.WriteLine("Named Entities:");
    foreach (var entity in response.Value)
    {
        Console.WriteLine($"\tText: {entity.Text},\tCategory: {entity.Category},\tSub-Category: {entity.SubCategory}");
        Console.WriteLine($"\t\tScore: {entity.ConfidenceScore:F2},\tLength: {entity.Length},\tOffset: {entity.Offset}\n");
    }
}

OutputOutput

Named Entities:
        Text: trip,     Category: Event,        Sub-Category:
                Score: 0.61,    Length: 4,      Offset: 18

        Text: Seattle,  Category: Location,     Sub-Category: GPE
                Score: 0.82,    Length: 7,      Offset: 26

        Text: last week,        Category: DateTime,     Sub-Category: DateRange
                Score: 0.80,    Length: 9,      Offset: 34

EntitätsverknüpfungEntity linking

Erstellen Sie eine neue Funktion namens EntityLinkingExample(), die den zuvor erstellten Client verwendet, rufen Sie die zugehörige Funktion RecognizeLinkedEntities() auf, und durchlaufen Sie die Ergebnisse.Create a new function called EntityLinkingExample() that takes the client that you created earlier, call its RecognizeLinkedEntities() function and iterate through the results. Das zurückgegebene Response<LinkedEntityCollection>-Objekt enthält die Sammlung der erkannten LinkedEntity-Entitäten.The returned Response<LinkedEntityCollection> object will contain the collection of detected entities LinkedEntity. Im Falle eines Fehlers wird ein Fehler vom Typ RequestFailedException ausgelöst.If there was an error, it will throw a RequestFailedException. Da verknüpfte Entitäten eindeutig identifiziert werden, werden Vorkommen der gleichen Entität unter einem Objekt vom Typ LinkedEntity als Liste von Objekten des Typs LinkedEntityMatch gruppiert.Since linked entities are uniquely identified, occurrences of the same entity are grouped under a LinkedEntity object as a list of LinkedEntityMatch objects.

static void EntityLinkingExample(TextAnalyticsClient client)
{
    var response = client.RecognizeLinkedEntities(
        "Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975, " +
        "to develop and sell BASIC interpreters for the Altair 8800. " +
        "During his career at Microsoft, Gates held the positions of chairman, " +
        "chief executive officer, president and chief software architect, " +
        "while also being the largest individual shareholder until May 2014.");
    Console.WriteLine("Linked Entities:");
    foreach (var entity in response.Value)
    {
        Console.WriteLine($"\tName: {entity.Name},\tID: {entity.DataSourceEntityId},\tURL: {entity.Url}\tData Source: {entity.DataSource}");
        Console.WriteLine("\tMatches:");
        foreach (var match in entity.Matches)
        {
            Console.WriteLine($"\t\tText: {match.Text}");
            Console.WriteLine($"\t\tScore: {match.ConfidenceScore:F2}");
            Console.WriteLine($"\t\tLength: {match.Length}");
            Console.WriteLine($"\t\tOffset: {match.Offset}\n");
        }
    }
}

OutputOutput

Linked Entities:
        Name: Microsoft,        ID: Microsoft,  URL: https://en.wikipedia.org/wiki/Microsoft    Data Source: Wikipedia
        Matches:
                Text: Microsoft
                Score: 0.55
                Length: 9
                Offset: 0

                Text: Microsoft
                Score: 0.55
                Length: 9
                Offset: 150

        Name: Bill Gates,       ID: Bill Gates, URL: https://en.wikipedia.org/wiki/Bill_Gates   Data Source: Wikipedia
        Matches:
                Text: Bill Gates
                Score: 0.63
                Length: 10
                Offset: 25

                Text: Gates
                Score: 0.63
                Length: 5
                Offset: 161

        Name: Paul Allen,       ID: Paul Allen, URL: https://en.wikipedia.org/wiki/Paul_Allen   Data Source: Wikipedia
        Matches:
                Text: Paul Allen
                Score: 0.60
                Length: 10
                Offset: 40

        Name: April 4,  ID: April 4,    URL: https://en.wikipedia.org/wiki/April_4      Data Source: Wikipedia
        Matches:
                Text: April 4
                Score: 0.32
                Length: 7
                Offset: 54

        Name: BASIC,    ID: BASIC,      URL: https://en.wikipedia.org/wiki/BASIC        Data Source: Wikipedia
        Matches:
                Text: BASIC
                Score: 0.33
                Length: 5
                Offset: 89

        Name: Altair 8800,      ID: Altair 8800,        URL: https://en.wikipedia.org/wiki/Altair_8800  Data Source: Wikipedia
        Matches:
                Text: Altair 8800
                Score: 0.88
                Length: 11
                Offset: 116

Erkennung von personenbezogenen InformationenPersonally Identifiable Information recognition

Erstellen Sie eine neue Funktion namens RecognizePIIExample(), die den zuvor erstellten Client verwendet, rufen Sie die zugehörige Funktion RecognizePiiEntities() auf, und durchlaufen Sie die Ergebnisse.Create a new function called RecognizePIIExample() that takes the client that you created earlier, call its RecognizePiiEntities() function and iterate through the results. Die zurückgegebene PiiEntityCollection stellt die Liste der erkannten PII-Entitäten dar.The returned PiiEntityCollection represents the list of detected PII entities. Im Falle eines Fehlers wird ein Fehler vom Typ RequestFailedException ausgelöst.If there was an error, it will throw a RequestFailedException.

static void RecognizePIIExample(TextAnalyticsClient client)
{
    string document = "A developer with SSN 859-98-0987 whose phone number is 800-102-1100 is building tools with our APIs.";

    PiiEntityCollection entities = client.RecognizePiiEntities(document).Value;

    Console.WriteLine($"Redacted Text: {entities.RedactedText}");
    if (entities.Count > 0)
    {
        Console.WriteLine($"Recognized {entities.Count} PII entit{(entities.Count > 1 ? "ies" : "y")}:");
        foreach (PiiEntity entity in entities)
        {
            Console.WriteLine($"Text: {entity.Text}, Category: {entity.Category}, SubCategory: {entity.SubCategory}, Confidence score: {entity.ConfidenceScore}");
        }
    }
    else
    {
        Console.WriteLine("No entities were found.");
    }
}

OutputOutput

Redacted Text: A developer with SSN *********** whose phone number is ************ is building tools with our APIs.
Recognized 2 PII entities:
Text: 859-98-0987, Category: U.S. Social Security Number (SSN), SubCategory: , Confidence score: 0.65
Text: 800-102-1100, Category: Phone Number, SubCategory: , Confidence score: 0.8

Erstellen Sie eine neue Funktion namens EntityRecognitionExample(), die den zuvor erstellten Client verwendet, rufen Sie die zugehörige Funktion RecognizeEntities() auf, und durchlaufen Sie die Ergebnisse.Create a new function called EntityRecognitionExample() that takes the client that you created earlier, call its RecognizeEntities() function and iterate through the results. Das zurückgegebene Objekt vom Typ Response<IReadOnlyCollection<CategorizedEntity>> enthält die Liste erkannter Entitäten.The returned Response<IReadOnlyCollection<CategorizedEntity>> object will contain the list of detected entities. Im Falle eines Fehlers wird ein Fehler vom Typ RequestFailedException ausgelöst.If there was an error, it will throw a RequestFailedException.

static void EntityRecognitionExample(TextAnalyticsClient client)
{
    var response = client.RecognizeEntities("I had a wonderful trip to Seattle last week.");
    Console.WriteLine("Named Entities:");
    foreach (var entity in response.Value)
    {
        Console.WriteLine($"\tText: {entity.Text},\tCategory: {entity.Category},\tSub-Category: {entity.SubCategory}");
        Console.WriteLine($"\t\tScore: {entity.ConfidenceScore:F2}\n");
    }
}

OutputOutput

Named Entities:
        Text: trip,     Category: Event,        Sub-Category:
                Score: 0.61

        Text: Seattle,  Category: Location,     Sub-Category: GPE
                Score: 0.82

        Text: last week,        Category: DateTime,     Sub-Category: DateRange
                Score: 0.80

EntitätsverknüpfungEntity linking

Erstellen Sie eine neue Funktion namens EntityLinkingExample(), die den zuvor erstellten Client verwendet, rufen Sie die zugehörige Funktion RecognizeLinkedEntities() auf, und durchlaufen Sie die Ergebnisse.Create a new function called EntityLinkingExample() that takes the client that you created earlier, call its RecognizeLinkedEntities() function and iterate through the results. Die Rückgabe (Response<IReadOnlyCollection<LinkedEntity>>) stellt die Liste der erkannten Entitäten dar.The returned Response<IReadOnlyCollection<LinkedEntity>> represents the list of detected entities. Im Falle eines Fehlers wird ein Fehler vom Typ RequestFailedException ausgelöst.If there was an error, it will throw a RequestFailedException. Da verknüpfte Entitäten eindeutig identifiziert werden, werden Vorkommen der gleichen Entität unter einem Objekt vom Typ LinkedEntity als Liste von Objekten des Typs LinkedEntityMatch gruppiert.Since linked entities are uniquely identified, occurrences of the same entity are grouped under a LinkedEntity object as a list of LinkedEntityMatch objects.

static void EntityLinkingExample(TextAnalyticsClient client)
{
    var response = client.RecognizeLinkedEntities(
        "Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975, " +
        "to develop and sell BASIC interpreters for the Altair 8800. " +
        "During his career at Microsoft, Gates held the positions of chairman, " +
        "chief executive officer, president and chief software architect, " +
        "while also being the largest individual shareholder until May 2014.");
    Console.WriteLine("Linked Entities:");
    foreach (var entity in response.Value)
    {
        Console.WriteLine($"\tName: {entity.Name},\tID: {entity.DataSourceEntityId},\tURL: {entity.Url}\tData Source: {entity.DataSource}");
        Console.WriteLine("\tMatches:");
        foreach (var match in entity.Matches)
        {
            Console.WriteLine($"\t\tText: {match.Text}");
            Console.WriteLine($"\t\tScore: {match.ConfidenceScore:F2}\n");
        }
    }
}

OutputOutput

Linked Entities:
        Name: Altair 8800,      ID: Altair 8800,        URL: https://en.wikipedia.org/wiki/Altair_8800  Data Source: Wikipedia
        Matches:
                Text: Altair 8800
                Score: 0.88

        Name: Bill Gates,       ID: Bill Gates, URL: https://en.wikipedia.org/wiki/Bill_Gates   Data Source: Wikipedia
        Matches:
                Text: Bill Gates
                Score: 0.63

                Text: Gates
                Score: 0.63

        Name: Paul Allen,       ID: Paul Allen, URL: https://en.wikipedia.org/wiki/Paul_Allen   Data Source: Wikipedia
        Matches:
                Text: Paul Allen
                Score: 0.60

        Name: Microsoft,        ID: Microsoft,  URL: https://en.wikipedia.org/wiki/Microsoft    Data Source: Wikipedia
        Matches:
                Text: Microsoft
                Score: 0.55

                Text: Microsoft
                Score: 0.55

        Name: April 4,  ID: April 4,    URL: https://en.wikipedia.org/wiki/April_4      Data Source: Wikipedia
        Matches:
                Text: April 4
                Score: 0.32

        Name: BASIC,    ID: BASIC,      URL: https://en.wikipedia.org/wiki/BASIC        Data Source: Wikipedia
        Matches:
                Text: BASIC
                Score: 0.33

Erstellen Sie eine neue Funktion namens RecognizeEntitiesExample(), die den zuvor erstellten Client verwendet, und rufen Sie die zugehörige Funktion Entities() auf.Create a new function called RecognizeEntitiesExample() that takes the client that you created earlier, and call its Entities() function. Durchlaufen Sie die Ergebnisse.Iterate through the results. Das zurückgegebene Objekt vom Typ EntitiesResult enthält bei erfolgreicher Ausführung die Liste der erkannten Entitäten in Entities und andernfalls errorMessage.The returned EntitiesResult object will contain the list of detected entities in Entities if successful, and an errorMessage if not. Geben Sie für jede erkannte Entität ihren Typ, Untertyp, Wikipedia-Namen (sofern vorhanden) sowie die Fundorte im Originaltext aus.For each detected entity, print its Type, Sub-Type, Wikipedia name (if they exist) as well as the locations in the original text.

static void entityRecognitionExample(ITextAnalyticsClient client)
{

    var result = client.Entities("Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975, to develop and sell BASIC interpreters for the Altair 8800.");
    Console.WriteLine("Entities:");
    foreach (var entity in result.Entities)
    {
        Console.WriteLine($"\tName: {entity.Name},\tType: {entity.Type ?? "N/A"},\tSub-Type: {entity.SubType ?? "N/A"}");
        foreach (var match in entity.Matches)
        {
            Console.WriteLine($"\t\tOffset: {match.Offset},\tLength: {match.Length},\tScore: {match.EntityTypeScore:F3}");
        }
    }
}

Erstellen Sie eine neue Funktion namens KeyPhraseExtractionExample(), die den zuvor erstellten Client verwendet, und rufen Sie die zugehörige Funktion ExtractKeyPhrases() auf.Create a new function called KeyPhraseExtractionExample() that takes the client that you created earlier, and call its ExtractKeyPhrases() function. Das zurückgegebene Objekt vom Typ <Response<KeyPhraseCollection> enthält die Liste erkannter Schlüsselbegriffe.The returned <Response<KeyPhraseCollection> object will contain the list of detected key phrases. Im Falle eines Fehlers wird ein Fehler vom Typ RequestFailedException ausgelöst.If there was an error, it will throw a RequestFailedException.

static void KeyPhraseExtractionExample(TextAnalyticsClient client)
{
    var response = client.ExtractKeyPhrases("My cat might need to see a veterinarian.");

    // Printing key phrases
    Console.WriteLine("Key phrases:");

    foreach (string keyphrase in response.Value)
    {
        Console.WriteLine($"\t{keyphrase}");
    }
}

OutputOutput

Key phrases:
    cat
    veterinarian

Erstellen Sie eine neue Funktion namens KeyPhraseExtractionExample(), die den zuvor erstellten Client verwendet, und rufen Sie die zugehörige Funktion ExtractKeyPhrases() auf.Create a new function called KeyPhraseExtractionExample() that takes the client that you created earlier, and call its ExtractKeyPhrases() function. Das zurückgegebene Objekt vom Typ <Response<IReadOnlyCollection<string>> enthält die Liste erkannter Schlüsselbegriffe.The returned <Response<IReadOnlyCollection<string>> object will contain the list of detected key phrases. Im Falle eines Fehlers wird ein Fehler vom Typ RequestFailedException ausgelöst.If there was an error, it will throw a RequestFailedException.

static void KeyPhraseExtractionExample(TextAnalyticsClient client)
{
    var response = client.ExtractKeyPhrases("My cat might need to see a veterinarian.");

    // Printing key phrases
    Console.WriteLine("Key phrases:");

    foreach (string keyphrase in response.Value)
    {
        Console.WriteLine($"\t{keyphrase}");
    }
}

OutputOutput

Key phrases:
    cat
    veterinarian

Erstellen Sie eine neue Funktion namens KeyPhraseExtractionExample(), die den zuvor erstellten Client verwendet, und rufen Sie die zugehörige Funktion KeyPhrases() auf.Create a new function called KeyPhraseExtractionExample() that takes the client that you created earlier and call its KeyPhrases() function. Das Ergebnis enthält bei erfolgreicher Ausführung die Liste der erkannten Schlüsselbegriffe in KeyPhrases und andernfalls errorMessage.The result will contain the list of detected key phrases in KeyPhrases if successful, and an errorMessage if not. Geben Sie alle erkannten Schlüsselbegriffe aus.Print any detected key phrases.

static void keyPhraseExtractionExample(TextAnalyticsClient client)
{
    var result = client.KeyPhrases("My cat might need to see a veterinarian.");

    // Printing key phrases
    Console.WriteLine("Key phrases:");

    foreach (string keyphrase in result.KeyPhrases)
    {
        Console.WriteLine($"\t{keyphrase}");
    }
}

OutputOutput

Key phrases:
    cat
    veterinarian

Erstellen Sie eine neue Funktion namens AnalyzeOperationExample(), die den zuvor erstellten Client verwendet, und rufen Sie die zugehörige Funktion StartAnalyzeOperationBatch() auf.Create a new function called AnalyzeOperationExample() that takes the client that you created earlier, and call its StartAnalyzeOperationBatch() function. Das zurückgegebene AnalyzeOperation-Objekt enthält das Operation-Schnittstellenobjekt für AnalyzeOperationResult.The returned AnalyzeOperation object will contain the Operation interface object for AnalyzeOperationResult. Da es sich um einen zeitintensiven Vorgang handelt, muss await in der operation.WaitForCompletionAsync()-Methode für den Wert aktualisiert werden.As it is a Long Running Operation, await on the operation.WaitForCompletionAsync() for the value to be updated. Sobald WaitForCompletionAsync() ausgeführt wurde, sollte die Sammlung in operation.Value aktualisiert werden.Once the WaitForCompletionAsync() is finishes, the collection should be updated in the operation.Value. Im Falle eines Fehlers wird ein Fehler vom Typ RequestFailedException ausgelöst.If there was an error, it will throw a RequestFailedException.

static async Task AnalyzeOperationExample(TextAnalyticsClient client)
{
    string inputText = "Microsoft was founded by Bill Gates and Paul Allen.";

    var batchDocuments = new List<string> { inputText };

    AnalyzeOperationOptions operationOptions = new AnalyzeOperationOptions()
    {
        EntitiesTaskParameters = new EntitiesTaskParameters(),
        DisplayName = "Analyze Operation Quick Start Example"
    };

    AnalyzeOperation operation = client.StartAnalyzeOperationBatch(batchDocuments, operationOptions, "en");

    await operation.WaitForCompletionAsync();

    AnalyzeOperationResult resultCollection = operation.Value;

    RecognizeEntitiesResultCollection entitiesResult = resultCollection.Tasks.EntityRecognitionTasks[0].Results;

    Console.WriteLine("Analyze Operation Request Details");
    Console.WriteLine($"    Status: {resultCollection.Status}");
    Console.WriteLine($"    DisplayName: {resultCollection.DisplayName}");
    Console.WriteLine("");

    Console.WriteLine("Recognized Entities");

    foreach (RecognizeEntitiesResult result in entitiesResult)
    {
        Console.WriteLine($"    Recognized the following {result.Entities.Count} entities:");

        foreach (CategorizedEntity entity in result.Entities)
        {
            Console.WriteLine($"    Entity: {entity.Text}");
            Console.WriteLine($"    Category: {entity.Category}");
            Console.WriteLine($"    Offset: {entity.Offset}");
            Console.WriteLine($"    ConfidenceScore: {entity.ConfidenceScore}");
            Console.WriteLine($"    SubCategory: {entity.SubCategory}");
        }
        Console.WriteLine("");
    }
}

Nachdem Sie das Beispiel zu Ihrer Anwendung hinzugefügt haben, können Sie es mithilfe von await in der Methode main() aufrufen.After you add this example to your application, call in your main() method using await.

await AnalyzeOperationExample(client).ConfigureAwait(false);

AusgabeOutput

Analyze Operation Request Details
    Status: succeeded
    DisplayName: Analyze Operation Quick Start Example

Recognized Entities
    Recognized the following 3 entities:
    Entity: Microsoft
    Category: Organization
    Offset: 0
    ConfidenceScore: 0.83
    SubCategory: 
    Entity: Bill Gates
    Category: Person
    Offset: 25
    ConfidenceScore: 0.85
    SubCategory: 
    Entity: Paul Allen
    Category: Person
    Offset: 40
    ConfidenceScore: 0.9
    SubCategory: 

Sie können den Analyze-Vorgang auch verwenden, um personenbezogene Informationen zu erkennen und Schlüsselbegriffe zu extrahieren.You can also use the Analyze operation to detect PII and key phrase extraction. Das Analyze-Beispiel finden Sie auf GitHub.See the Analyze sample on GitHub.

Dieses Feature ist in Version 3.0 nicht verfügbar.This feature is not available in version 3.0.

Dieses Feature ist in Version 2.1 nicht verfügbar.This feature is not available in version 2.1.

Referenzdokumentation | Quellcode der Bibliothek | Paket | BeispieleReference documentation | Library source code | Package | Samples

Referenzdokumentation | Quellcode der Bibliothek | Paket | BeispieleReference documentation | Library source code | Package | Samples

In diesem Artikel wird nur Version 3.x der API beschrieben.This article only describes version 3.x of the API.

Erstellen Sie ein Maven-Projekt in Ihrer bevorzugten IDE oder Entwicklungsumgebung.Create a Maven project in your preferred IDE or development environment. Fügen Sie anschließend der Datei pom.xml Ihres Projekts die folgende Abhängigkeit hinzu.Then add the following dependency to your project's pom.xml file. Die Implementierungssyntax für andere Buildtools ist online verfügbar.You can find the implementation syntax for other build tools online.

<dependencies>
     <dependency>
        <groupId>com.azure</groupId>
        <artifactId>azure-ai-textanalytics</artifactId>
        <version>5.1.0-beta.3</version>
    </dependency>
</dependencies>

Erstellen Sie ein Maven-Projekt in Ihrer bevorzugten IDE oder Entwicklungsumgebung.Create a Maven project in your preferred IDE or development environment. Fügen Sie anschließend der Datei pom.xml Ihres Projekts die folgende Abhängigkeit hinzu.Then add the following dependency to your project's pom.xml file. Die Implementierungssyntax für andere Buildtools ist online verfügbar.You can find the implementation syntax for other build tools online.

<dependencies>
     <dependency>
        <groupId>com.azure</groupId>
        <artifactId>azure-ai-textanalytics</artifactId>
        <version>5.0.0</version>
    </dependency>
</dependencies>

In diesem Artikel wird nur Version 3.x der API beschrieben.This article only describes version 3.x of the API.

public static void main(String[] args) {
    //You will create these methods later in the quickstart.
    TextAnalyticsClient client = authenticateClient(KEY, ENDPOINT);

    sentimentAnalysisWithOpinionMiningExample(client)
    detectLanguageExample(client);
    recognizeEntitiesExample(client);
    recognizeLinkedEntitiesExample(client);
    recognizePiiEntitiesExample(client);
    extractKeyPhrasesExample(client);
}

public static void main(String[] args) {
    //You will create these methods later in the quickstart.
    TextAnalyticsClient client = authenticateClient(KEY, ENDPOINT);

    sentimentAnalysisExample(client);
    detectLanguageExample(client);
    recognizeEntitiesExample(client);
    recognizeLinkedEntitiesExample(client);
    extractKeyPhrasesExample(client);
        AnalyzeOperationExample(client)
}

In diesem Artikel wird nur Version 3.x der API beschrieben.This article only describes version 3.x of the API.

Erstellen Sie eine neue Funktion namens sentimentAnalysisExample(), die den zuvor erstellten Client verwendet, und rufen Sie die zugehörige Funktion analyzeSentiment() auf.Create a new function called sentimentAnalysisExample() that takes the client that you created earlier, and call its analyzeSentiment() function. Das zurückgegebene Objekt AnalyzeSentimentResult enthält documentSentiment und sentenceSentiments (sofern erfolgreich). Andernfalls enthält es eine Fehlermeldung (errorMessage).The returned AnalyzeSentimentResult object will contain documentSentiment and sentenceSentiments if successful, or an errorMessage if not.

static void sentimentAnalysisExample(TextAnalyticsClient client)
{
    // The text that need be analyzed.
    String text = "I had the best day of my life. I wish you were there with me.";

    DocumentSentiment documentSentiment = client.analyzeSentiment(text);
    System.out.printf(
        "Recognized document sentiment: %s, positive score: %s, neutral score: %s, negative score: %s.%n",
        documentSentiment.getSentiment(),
        documentSentiment.getConfidenceScores().getPositive(),
        documentSentiment.getConfidenceScores().getNeutral(),
        documentSentiment.getConfidenceScores().getNegative());

    for (SentenceSentiment sentenceSentiment : documentSentiment.getSentences()) {
        System.out.printf(
            "Recognized sentence sentiment: %s, positive score: %s, neutral score: %s, negative score: %s.%n",
            sentenceSentiment.getSentiment(),
            sentenceSentiment.getConfidenceScores().getPositive(),
            sentenceSentiment.getConfidenceScores().getNeutral(),
            sentenceSentiment.getConfidenceScores().getNegative());
        }
    }
}

OutputOutput

Recognized document sentiment: positive, positive score: 1.0, neutral score: 0.0, negative score: 0.0.
Recognized sentence sentiment: positive, positive score: 1.0, neutral score: 0.0, negative score: 0.0.
Recognized sentence sentiment: neutral, positive score: 0.21, neutral score: 0.77, negative score: 0.02.

Opinion MiningOpinion mining

Zum Durchführen von Stimmungsanalyse mit Opinion Mining erstellen Sie eine neue Funktion mit dem Namen sentimentAnalysisWithOpinionMiningExample(), die den von Ihnen zuvor erstellten Client akzeptiert, und rufen seine analyzeSentiment()-Funktion mit dem Einstellungsoptionsobjekt AnalyzeSentimentOptions auf.To perform sentiment analysis with opinion mining, create a new function called sentimentAnalysisWithOpinionMiningExample() that takes the client that you created earlier, and call its analyzeSentiment() function with setting option object AnalyzeSentimentOptions. Das zurückgegebene Objekt AnalyzeSentimentResult enthält documentSentiment und sentenceSentiments (sofern erfolgreich). Andernfalls enthält es eine Fehlermeldung (errorMessage).The returned AnalyzeSentimentResult object will contain documentSentiment and sentenceSentiments if successful, or an errorMessage if not.

static void sentimentAnalysisWithOpinionMiningExample(TextAnalyticsClient client)
{
    // The document that needs be analyzed.
    String document = "Bad atmosphere. Not close to plenty of restaurants, hotels, and transit! Staff are not friendly and helpful.";

    System.out.printf("Document = %s%n", document);

    AnalyzeSentimentOptions options = new AnalyzeSentimentOptions().setIncludeOpinionMining(true);
    final DocumentSentiment documentSentiment = client.analyzeSentiment(document, "en", options);
    SentimentConfidenceScores scores = documentSentiment.getConfidenceScores();
    System.out.printf(
            "Recognized document sentiment: %s, positive score: %f, neutral score: %f, negative score: %f.%n",
            documentSentiment.getSentiment(), scores.getPositive(), scores.getNeutral(), scores.getNegative());

    documentSentiment.getSentences().forEach(sentenceSentiment -> {
        SentimentConfidenceScores sentenceScores = sentenceSentiment.getConfidenceScores();
        System.out.printf("\tSentence sentiment: %s, positive score: %f, neutral score: %f, negative score: %f.%n",
                sentenceSentiment.getSentiment(), sentenceScores.getPositive(), sentenceScores.getNeutral(), sentenceScores.getNegative());
        sentenceSentiment.getMinedOpinions().forEach(minedOpinions -> {
            AspectSentiment aspectSentiment = minedOpinions.getAspect();
            System.out.printf("\t\tAspect sentiment: %s, aspect text: %s%n", aspectSentiment.getSentiment(),
                    aspectSentiment.getText());
            SentimentConfidenceScores aspectScores = aspectSentiment.getConfidenceScores();
            System.out.printf("\t\tAspect positive score: %f, negative score: %f.%n",
                    aspectScores.getPositive(), aspectScores.getNegative());
            for (OpinionSentiment opinionSentiment : minedOpinions.getOpinions()) {
                System.out.printf("\t\t\t'%s' opinion sentiment because of \"%s\". Is the opinion negated: %s.%n",
                        opinionSentiment.getSentiment(), opinionSentiment.getText(), opinionSentiment.isNegated());
                SentimentConfidenceScores opinionScores = opinionSentiment.getConfidenceScores();
                System.out.printf("\t\t\tOpinion positive score: %f, negative score: %f.%n",
                        opinionScores.getPositive(), opinionScores.getNegative());
            }
        });
    });
}

OutputOutput

Document = Bad atmosphere. Not close to plenty of restaurants, hotels, and transit! Staff are not friendly and helpful.
Recognized document sentiment: negative, positive score: 0.010000, neutral score: 0.140000, negative score: 0.850000.
    Sentence sentiment: negative, positive score: 0.000000, neutral score: 0.000000, negative score: 1.000000.
        Aspect sentiment: negative, aspect text: atmosphere
        Aspect positive score: 0.010000, negative score: 0.990000.
            'negative' opinion sentiment because of "bad". Is the opinion negated: false.
            Opinion positive score: 0.010000, negative score: 0.990000.
    Sentence sentiment: negative, positive score: 0.020000, neutral score: 0.440000, negative score: 0.540000.
    Sentence sentiment: negative, positive score: 0.000000, neutral score: 0.000000, negative score: 1.000000.
        Aspect sentiment: negative, aspect text: Staff
        Aspect positive score: 0.000000, negative score: 1.000000.
            'negative' opinion sentiment because of "friendly". Is the opinion negated: true.
            Opinion positive score: 0.000000, negative score: 1.000000.
            'negative' opinion sentiment because of "helpful". Is the opinion negated: true.
            Opinion positive score: 0.000000, negative score: 1.000000.

Process finished with exit code 0

Erstellen Sie eine neue Funktion namens sentimentAnalysisExample(), die den zuvor erstellten Client verwendet, und rufen Sie die zugehörige Funktion analyzeSentiment() auf.Create a new function called sentimentAnalysisExample() that takes the client that you created earlier, and call its analyzeSentiment() function. Das zurückgegebene Objekt AnalyzeSentimentResult enthält documentSentiment und sentenceSentiments (sofern erfolgreich). Andernfalls enthält es eine Fehlermeldung (errorMessage).The returned AnalyzeSentimentResult object will contain documentSentiment and sentenceSentiments if successful, or an errorMessage if not.

static void sentimentAnalysisExample(TextAnalyticsClient client)
{
    // The text that need be analyzed.
    String text = "I had the best day of my life. I wish you were there with me.";

    DocumentSentiment documentSentiment = client.analyzeSentiment(text);
    System.out.printf(
        "Recognized document sentiment: %s, positive score: %s, neutral score: %s, negative score: %s.%n",
        documentSentiment.getSentiment(),
        documentSentiment.getConfidenceScores().getPositive(),
        documentSentiment.getConfidenceScores().getNeutral(),
        documentSentiment.getConfidenceScores().getNegative());

    for (SentenceSentiment sentenceSentiment : documentSentiment.getSentences()) {
        System.out.printf(
            "Recognized sentence sentiment: %s, positive score: %s, neutral score: %s, negative score: %s.%n",
            sentenceSentiment.getSentiment(),
            sentenceSentiment.getConfidenceScores().getPositive(),
            sentenceSentiment.getConfidenceScores().getNeutral(),
            sentenceSentiment.getConfidenceScores().getNegative());
        }
    }
}

OutputOutput

Recognized document sentiment: positive, positive score: 1.0, neutral score: 0.0, negative score: 0.0.
Recognized sentence sentiment: positive, positive score: 1.0, neutral score: 0.0, negative score: 0.0.
Recognized sentence sentiment: neutral, positive score: 0.21, neutral score: 0.77, negative score: 0.02.

In diesem Artikel wird nur Version 3.x der API beschrieben.This article only describes version 3.x of the API.

Erstellen Sie eine neue Funktion namens recognizeEntitiesExample(), die den zuvor erstellten Client verwendet, und rufen Sie die zugehörige Funktion recognizeEntities() auf.Create a new function called recognizeEntitiesExample() that takes the client that you created earlier, and call its recognizeEntities() function. Das zurückgegebene Objekt CategorizedEntityCollection enthält eine Liste mit Elementen vom Typ CategorizedEntity (sofern erfolgreich). Andernfalls enthält es eine Fehlermeldung (errorMessage).The returned CategorizedEntityCollection object will contain a list of CategorizedEntity if successful, or an errorMessage if not.

static void recognizeEntitiesExample(TextAnalyticsClient client)
{
    // The text that need be analyzed.
    String text = "I had a wonderful trip to Seattle last week.";

    for (CategorizedEntity entity : client.recognizeEntities(text)) {
        System.out.printf(
            "Recognized entity: %s, entity category: %s, entity sub-category: %s, score: %s, offset: %s, length: %s.%n",
            entity.getText(),
            entity.getCategory(),
            entity.getSubcategory(),
            entity.getConfidenceScore(),
            entity.getOffset(),
            entity.getLength());
    }
}

OutputOutput

Recognized entity: trip, entity category: Event, entity sub-category: null, score: 0.61, offset: 8, length: 4.
Recognized entity: Seattle, entity category: Location, entity sub-category: GPE, score: 0.82, offset: 16, length: 7.
Recognized entity: last week, entity category: DateTime, entity sub-category: DateRange, score: 0.8, offset: 24, length: 9.

EntitätsverknüpfungEntity linking

Erstellen Sie eine neue Funktion namens recognizeLinkedEntitiesExample(), die den zuvor erstellten Client verwendet, und rufen Sie die zugehörige Funktion recognizeLinkedEntities() auf.Create a new function called recognizeLinkedEntitiesExample() that takes the client that you created earlier, and call its recognizeLinkedEntities() function. Das zurückgegebene Objekt LinkedEntityCollection enthält eine Liste mit Elementen vom Typ LinkedEntity (sofern erfolgreich). Andernfalls enthält es eine Fehlermeldung (errorMessage).The returned LinkedEntityCollection object will contain a list of LinkedEntity if successful, or an errorMessage if not. Da verknüpfte Entitäten eindeutig identifiziert werden, werden Vorkommen der gleichen Entität unter einem Objekt vom Typ LinkedEntity als Liste von Objekten des Typs LinkedEntityMatch gruppiert.Since linked entities are uniquely identified, occurrences of the same entity are grouped under a LinkedEntity object as a list of LinkedEntityMatch objects.

static void recognizeLinkedEntitiesExample(TextAnalyticsClient client)
{
    // The text that need be analyzed.
    String text = "Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975, " +
        "to develop and sell BASIC interpreters for the Altair 8800. " +
        "During his career at Microsoft, Gates held the positions of chairman, " +
        "chief executive officer, president and chief software architect, " +
        "while also being the largest individual shareholder until May 2014.";

    System.out.printf("Linked Entities:%n");
    for (LinkedEntity linkedEntity : client.recognizeLinkedEntities(text)) {
        System.out.printf("Name: %s, ID: %s, URL: %s, Data Source: %s.%n",
            linkedEntity.getName(),
            linkedEntity.getDataSourceEntityId(),
            linkedEntity.getUrl(),
            linkedEntity.getDataSource());
        System.out.printf("Matches:%n");
        for (LinkedEntityMatch linkedEntityMatch : linkedEntity.getMatches()) {
            System.out.printf("Text: %s, Score: %.2f, Offset: %s, Length: %s%n",
            linkedEntityMatch.getText(),
            linkedEntityMatch.getConfidenceScore(),
            linkedEntityMatch.getOffset(),
            linkedEntityMatch.getLength());
        }
    }
}

OutputOutput

Linked Entities:
Name: Microsoft, ID: Microsoft, URL: https://en.wikipedia.org/wiki/Microsoft, Data Source: Wikipedia.
Matches:
Text: Microsoft, Score: 0.55, Offset: 9, Length: 0
Text: Microsoft, Score: 0.55, Offset: 9, Length: 150
Name: Bill Gates, ID: Bill Gates, URL: https://en.wikipedia.org/wiki/Bill_Gates, Data Source: Wikipedia.
Matches:
Text: Bill Gates, Score: 0.63, Offset: 10, Length: 25
Text: Gates, Score: 0.63, Offset: 5, Length: 161
Name: Paul Allen, ID: Paul Allen, URL: https://en.wikipedia.org/wiki/Paul_Allen, Data Source: Wikipedia.
Matches:
Text: Paul Allen, Score: 0.60, Offset: 10, Length: 40
Name: April 4, ID: April 4, URL: https://en.wikipedia.org/wiki/April_4, Data Source: Wikipedia.
Matches:
Text: April 4, Score: 0.32, Offset: 7, Length: 54
Name: BASIC, ID: BASIC, URL: https://en.wikipedia.org/wiki/BASIC, Data Source: Wikipedia.
Matches:
Text: BASIC, Score: 0.33, Offset: 5, Length: 89
Name: Altair 8800, ID: Altair 8800, URL: https://en.wikipedia.org/wiki/Altair_8800, Data Source: Wikipedia.
Matches:
Text: Altair 8800, Score: 0.88, Offset: 11, Length: 116

Erkennung von personenbezogenen InformationenPersonally Identifiable Information Recognition

Erstellen Sie eine neue Funktion namens recognizePiiEntitiesExample(), die den zuvor erstellten Client verwendet, und rufen Sie die zugehörige Funktion recognizePiiEntities() auf.Create a new function called recognizePiiEntitiesExample() that takes the client that you created earlier, and call its recognizePiiEntities() function. Das zurückgegebene Objekt PiiEntityCollection enthält eine Liste mit Elementen vom Typ PiiEntity (sofern erfolgreich). Andernfalls enthält es eine Fehlermeldung (errorMessage).The returned PiiEntityCollection object will contain a list of PiiEntity if successful, or an errorMessage if not. Es enthält außerdem den bearbeiteten Text, der aus dem Eingabetext besteht, in dem alle identifizierbaren Entitäten durch ***** ersetzt wurden.It will also contain the redacted text, which consists of the input text with all identifiable entities replaced with *****.

static void recognizePiiEntitiesExample(TextAnalyticsClient client)
{
    // The text that need be analyzed.
    String document = "My SSN is 859-98-0987";
    PiiEntityCollection piiEntityCollection = client.recognizePiiEntities(document);
    System.out.printf("Redacted Text: %s%n", piiEntityCollection.getRedactedText());
    piiEntityCollection.forEach(entity -> System.out.printf(
        "Recognized Personally Identifiable Information entity: %s, entity category: %s, entity subcategory: %s,"
            + " confidence score: %f.%n",
        entity.getText(), entity.getCategory(), entity.getSubcategory(), entity.getConfidenceScore()));
}

OutputOutput

Redacted Text: My SSN is ***********
Recognized Personally Identifiable Information entity: 859-98-0987, entity category: U.S. Social Security Number (SSN), entity subcategory: null, confidence score: 0.650000.

Erstellen Sie eine neue Funktion namens recognizeEntitiesExample(), die den zuvor erstellten Client verwendet, und rufen Sie die zugehörige Funktion recognizeEntities() auf.Create a new function called recognizeEntitiesExample() that takes the client that you created earlier, and call its recognizeEntities() function. Das zurückgegebene Objekt CategorizedEntityCollection enthält eine Liste mit Elementen vom Typ CategorizedEntity (sofern erfolgreich). Andernfalls enthält es eine Fehlermeldung (errorMessage).The returned CategorizedEntityCollection object will contain a list of CategorizedEntity if successful, or an errorMessage if not.

static void recognizeEntitiesExample(TextAnalyticsClient client)
{
    // The text that need be analyzed.
    String text = "I had a wonderful trip to Seattle last week.";

    for (CategorizedEntity entity : client.recognizeEntities(text)) {
        System.out.printf(
            "Recognized entity: %s, entity category: %s, entity sub-category: %s, score: %s.%n",
            entity.getText(),
            entity.getCategory(),
            entity.getSubcategory(),
            entity.getConfidenceScore());
    }
}

OutputOutput

Recognized entity: trip, entity category: Event, entity sub-category: null, score: 0.61.
Recognized entity: Seattle, entity category: Location, entity sub-category: GPE, score: 0.82.
Recognized entity: last week, entity category: DateTime, entity sub-category: DateRange, score: 0.8.

EntitätsverknüpfungEntity linking

Erstellen Sie eine neue Funktion namens recognizeLinkedEntitiesExample(), die den zuvor erstellten Client verwendet, und rufen Sie die zugehörige Funktion recognizeLinkedEntities() auf.Create a new function called recognizeLinkedEntitiesExample() that takes the client that you created earlier, and call its recognizeLinkedEntities() function. Das zurückgegebene Objekt LinkedEntityCollection enthält eine Liste mit Elementen vom Typ LinkedEntity (sofern erfolgreich). Andernfalls enthält es eine Fehlermeldung (errorMessage).The returned LinkedEntityCollection object will contain a list of LinkedEntity if successful, or an errorMessage if not. Da verknüpfte Entitäten eindeutig identifiziert werden, werden Vorkommen der gleichen Entität unter einem Objekt vom Typ LinkedEntity als Liste von Objekten des Typs LinkedEntityMatch gruppiert.Since linked entities are uniquely identified, occurrences of the same entity are grouped under a LinkedEntity object as a list of LinkedEntityMatch objects.

static void recognizeLinkedEntitiesExample(TextAnalyticsClient client)
{
    // The text that need be analyzed.
    String text = "Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975, " +
        "to develop and sell BASIC interpreters for the Altair 8800. " +
        "During his career at Microsoft, Gates held the positions of chairman, " +
        "chief executive officer, president and chief software architect, " +
        "while also being the largest individual shareholder until May 2014.";

    System.out.printf("Linked Entities:%n");
    for (LinkedEntity linkedEntity : client.recognizeLinkedEntities(text)) {
        System.out.printf("Name: %s, ID: %s, URL: %s, Data Source: %s.%n",
            linkedEntity.getName(),
            linkedEntity.getDataSourceEntityId(),
            linkedEntity.getUrl(),
            linkedEntity.getDataSource());
        System.out.printf("Matches:%n");
        for (LinkedEntityMatch linkedEntityMatch : linkedEntity.getMatches()) {
            System.out.printf("Text: %s, Score: %.2f%n",
            linkedEntityMatch.getText(),
            linkedEntityMatch.getConfidenceScore());
        }
    }
}

OutputOutput

Linked Entities:
Name: Altair 8800, ID: Altair 8800, URL: https://en.wikipedia.org/wiki/Altair_8800, Data Source: Wikipedia.
Matches:
Text: Altair 8800, Score: 0.88
Name: Bill Gates, ID: Bill Gates, URL: https://en.wikipedia.org/wiki/Bill_Gates, Data Source: Wikipedia.
Matches:
Text: Bill Gates, Score: 0.63
Text: Gates, Score: 0.63
Name: Paul Allen, ID: Paul Allen, URL: https://en.wikipedia.org/wiki/Paul_Allen, Data Source: Wikipedia.
Matches:
Text: Paul Allen, Score: 0.60
Name: Microsoft, ID: Microsoft, URL: https://en.wikipedia.org/wiki/Microsoft, Data Source: Wikipedia.
Matches:
Text: Microsoft, Score: 0.55
Text: Microsoft, Score: 0.55
Name: April 4, ID: April 4, URL: https://en.wikipedia.org/wiki/April_4, Data Source: Wikipedia.
Matches:
Text: April 4, Score: 0.32
Name: BASIC, ID: BASIC, URL: https://en.wikipedia.org/wiki/BASIC, Data Source: Wikipedia.
Matches:
Text: BASIC, Score: 0.33

In diesem Artikel wird nur Version 3.x der API beschrieben.This article only describes version 3.x of the API.

Erstellen Sie die neue Funktion analyzeOperationExample(), die die Funktion beginAnalyzeTasks() aufruft.Create a new function called analyzeOperationExample(), which calls the beginAnalyzeTasks() function. Daraus resultiert ein zeitintensiver Vorgang, aus dem Ergebnisse abgerufen werden.The result will be a long running operation which will be polled for results.

static void analyzeOperationExample(TextAnalyticsClient client)
{
        List<TextDocumentInput> documents = Arrays.asList(
                        new TextDocumentInput("0", "Microsoft was founded by Bill Gates and Paul Allen.")
                        );

        SyncPoller<TextAnalyticsOperationResult, PagedIterable<AnalyzeTasksResult>> syncPoller =
                        client.beginAnalyzeTasks(documents,
                                        new AnalyzeTasksOptions().setDisplayName("{tasks_display_name}")
                                                        .setEntitiesRecognitionTasks(Arrays.asList(new EntitiesTask())),
                                        Context.NONE);

        syncPoller.waitForCompletion();
        PagedIterable<AnalyzeTasksResult> result = syncPoller.getFinalResult();

        result.forEach(analyzeJobState -> {
                System.out.printf("Job Display Name: %s, Job ID: %s.%n", analyzeJobState.getDisplayName(),
                                analyzeJobState.getJobId());
                System.out.printf("Total tasks: %s, completed: %s, failed: %s, in progress: %s.%n",
                                analyzeJobState.getTotal(), analyzeJobState.getCompleted(), analyzeJobState.getFailed(),
                                analyzeJobState.getInProgress());

                List<RecognizeEntitiesResultCollection> entityRecognitionTasks =
                                analyzeJobState.getEntityRecognitionTasks();
                if (entityRecognitionTasks != null) {
                        entityRecognitionTasks.forEach(taskResult -> {
                                // Recognized entities for each of documents from a batch of documents
                                AtomicInteger counter = new AtomicInteger();
                                for (RecognizeEntitiesResult entitiesResult : taskResult) {
                                        System.out.printf("%n%s%n", documents.get(counter.getAndIncrement()));
                                        if (entitiesResult.isError()) {
                                                // Erroneous document
                                                System.out.printf("Cannot recognize entities. Error: %s%n",
                                                                entitiesResult.getError().getMessage());
                                        } else {
                                                // Valid document
                                                entitiesResult.getEntities().forEach(entity -> System.out.printf(
                                                                "Recognized entity: %s, entity category: %s, entity subcategory: %s, "
                                                                                + "confidence score: %f.%n",
                                                                entity.getText(), entity.getCategory(), entity.getSubcategory(),
                                                                entity.getConfidenceScore()));
                                        }
                                }
                        });
                }
        });
    }

Nachdem Sie das Beispiel zu Ihrer Anwendung hinzugefügt haben, können Sie es in der Methode main() aufrufen.After you add this example to your application, call it in your main() method.

analyzeOperationExample(client);

AusgabeOutput

Job Display Name: {tasks_display_name}, Job ID: 84fd4db4-0734-47ec-b263-ac5451e83f2a_637432416000000000.
Total tasks: 1, completed: 1, failed: 0, in progress: 0.

Text = Microsoft was founded by Bill Gates and Paul Allen., Id = 0, Language = null
Recognized entity: Microsoft, entity category: Organization, entity subcategory: null, confidence score: 0.960000.
Recognized entity: Bill Gates, entity category: Person, entity subcategory: null, confidence score: 1.000000.
Recognized entity: Paul Allen, entity category: Person, entity subcategory: null, confidence score: 0.990000.

Sie können den Analyze-Vorgang auch verwenden, um personenbezogene Informationen zu erkennen und Schlüsselbegriffe zu extrahieren.You can also use the Analyze operation to detect PII and key phrase extraction. Das Analyze-Beispiel finden Sie auf GitHub.See the Analyze sample on GitHub.

Dieses Feature ist in Version 3.0 nicht verfügbar.This feature is not available in version 3.0.

Dieses Feature ist in Version 2.1 nicht verfügbar.This feature is not available in version 2.1.

v3-Referenzdokumentation | Quellcode der v3-Bibliothek | v3-Paket (NPM) | v3-Beispielev3 Reference documentation | v3 Library source code | v3 Package (NPM) | v3 Samples

v3-Referenzdokumentation | Quellcode der v3-Bibliothek | v3-Paket (NPM) | v3-Beispielev3 Reference documentation | v3 Library source code | v3 Package (NPM) | v3 Samples

v2-Referenzdokumentation | Quellcode der v2-Bibliothek | v2-Paket (NPM) | v2-Beispielev2 Reference documentation | v2 Library source code | v2 Package (NPM) | v2 Samples

Installieren Sie die NPM-Pakete vom Typ @azure/ai-text-analytics:Install the @azure/ai-text-analytics NPM packages:

npm install --save @azure/ai-text-analytics@5.1.0-beta.3

Installieren Sie die NPM-Pakete vom Typ @azure/ai-text-analytics:Install the @azure/ai-text-analytics NPM packages:

npm install --save @azure/ai-text-analytics@5.0.0

Installieren Sie die NPM-Pakete vom Typ @azure/cognitiveservices-textanalytics:Install the @azure/cognitiveservices-textanalytics NPM packages:

npm install --save @azure/cognitiveservices-textanalytics @azure/ms-rest-js

"use strict";

const { TextAnalyticsClient, AzureKeyCredential } = require("@azure/ai-text-analytics");

"use strict";

const { TextAnalyticsClient, AzureKeyCredential } = require("@azure/ai-text-analytics");

"use strict";

const os = require("os");
const CognitiveServicesCredentials = require("@azure/ms-rest-js");
const TextAnalyticsAPIClient = require("@azure/cognitiveservices-textanalytics");

Erstellen Sie ein neues Objekt vom Typ TextAnalyticsClient mit Ihrem Schlüssel und Ihrem Endpunkt als Parameter.Create a new TextAnalyticsClient object with your key and endpoint as parameters.

const textAnalyticsClient = new TextAnalyticsClient(endpoint,  new AzureKeyCredential(key));

Erstellen Sie ein neues Objekt vom Typ TextAnalyticsClient mit Ihrem Schlüssel und Ihrem Endpunkt als Parameter.Create a new TextAnalyticsClient object with your key and endpoint as parameters.

const textAnalyticsClient = new TextAnalyticsClient(endpoint,  new AzureKeyCredential(key));

Erstellen Sie ein TextAnalyticsClient-Objekt mit credentials und endpoint als Parameter.Create a new TextAnalyticsClient object with credentials and endpoint as a parameter.

const textAnalyticsClient = new TextAnalyticsClient(endpoint, new AzureKeyCredential(key));

Erstellen Sie ein Zeichenfolgenarray, das das zu analysierende Dokument enthält.Create an array of strings containing the document you want to analyze. Rufen Sie die Methode analyzeSentiment() des Clients auf, und rufen Sie das zurückgegebene Objekt SentimentBatchResult ab.Call the client's analyzeSentiment() method and get the returned SentimentBatchResult object. Durchlaufen Sie die Liste mit den Ergebnissen, und geben Sie die ID des jeweiligen Dokuments sowie die Stimmung auf Dokumentebene zusammen mit Zuverlässigkeitsbewertungen aus.Iterate through the list of results, and print each document's ID, document level sentiment with confidence scores. Das Ergebnis enthält die Stimmung auf Satzebene sowie Offsets, Länge und Zuverlässigkeitsbewertungen für jedes Dokument.For each document, result contains sentence level sentiment along with offsets, length, and confidence scores.

async function sentimentAnalysis(client){

    const sentimentInput = [
        "I had the best day of my life. I wish you were there with me."
    ];
    const sentimentResult = await client.analyzeSentiment(sentimentInput);

    sentimentResult.forEach(document => {
        console.log(`ID: ${document.id}`);
        console.log(`\tDocument Sentiment: ${document.sentiment}`);
        console.log(`\tDocument Scores:`);
        console.log(`\t\tPositive: ${document.confidenceScores.positive.toFixed(2)} \tNegative: ${document.confidenceScores.negative.toFixed(2)} \tNeutral: ${document.confidenceScores.neutral.toFixed(2)}`);
        console.log(`\tSentences Sentiment(${document.sentences.length}):`);
        document.sentences.forEach(sentence => {
            console.log(`\t\tSentence sentiment: ${sentence.sentiment}`)
            console.log(`\t\tSentences Scores:`);
            console.log(`\t\tPositive: ${sentence.confidenceScores.positive.toFixed(2)} \tNegative: ${sentence.confidenceScores.negative.toFixed(2)} \tNeutral: ${sentence.confidenceScores.neutral.toFixed(2)}`);
        });
    });
}
sentimentAnalysis(textAnalyticsClient)

Führen Sie in Ihrem Konsolenfenster Ihren Code mit node index.js aus.Run your code with node index.js in your console window.

OutputOutput

ID: 0
        Document Sentiment: positive
        Document Scores:
                Positive: 1.00  Negative: 0.00  Neutral: 0.00
        Sentences Sentiment(2):
                Sentence sentiment: positive
                Sentences Scores:
                Positive: 1.00  Negative: 0.00  Neutral: 0.00
                Sentence sentiment: neutral
                Sentences Scores:
                Positive: 0.21  Negative: 0.02  Neutral: 0.77

Opinion MiningOpinion mining

Zum Durchführen von Stimmungsanalyse mit Opinion Mining erstellen Sie ein Array aus Zeichenfolgen, die das zu analysierende Dokument enthalten.In order to do sentiment analysis with opinion mining, create an array of strings containing the document you want to analyze. Rufen Sie die Methode analyzeSentiment() des Clients mit hinzugefügtem Optionsflag includeOpinionMining: true auf, und rufen Sie das zurückgegebene SentimentBatchResult-Objekt ab.Call the client's analyzeSentiment() method with adding option flag includeOpinionMining: true and get the returned SentimentBatchResult object. Durchlaufen Sie die Liste mit den Ergebnissen, und geben Sie die ID des jeweiligen Dokuments sowie die Stimmung auf Dokumentebene zusammen mit Zuverlässigkeitsbewertungen aus.Iterate through the list of results, and print each document's ID, document level sentiment with confidence scores. Für jedes Dokument enthält das Ergebnis nicht nur die Stimmung auf Satzebene wie oben sondern auch die Stimmung auf Ansichts- und Meinungsebene.For each document, result contains not only sentence level sentiment as above, but also aspect and opinion level sentiment.

async function sentimentAnalysisWithOpinionMining(client){

    const sentimentInput = [
        {
            text: "The food and service were unacceptable, but the concierge were nice",
            id: "0",
            language: "en"
        }
    ];
    const sentimentResult = await client.analyzeSentiment(sentimentInput, { includeOpinionMining: true });

    sentimentResult.forEach(document => {
        console.log(`ID: ${document.id}`);
        console.log(`\tDocument Sentiment: ${document.sentiment}`);
        console.log(`\tDocument Scores:`);
        console.log(`\t\tPositive: ${document.confidenceScores.positive.toFixed(2)} \tNegative: ${document.confidenceScores.negative.toFixed(2)} \tNeutral: ${document.confidenceScores.neutral.toFixed(2)}`);
        console.log(`\tSentences Sentiment(${document.sentences.length}):`);
        document.sentences.forEach(sentence => {
            console.log(`\t\tSentence sentiment: ${sentence.sentiment}`)
            console.log(`\t\tSentences Scores:`);
            console.log(`\t\tPositive: ${sentence.confidenceScores.positive.toFixed(2)} \tNegative: ${sentence.confidenceScores.negative.toFixed(2)} \tNeutral: ${sentence.confidenceScores.neutral.toFixed(2)}`);
            console.log("\tMined opinions");
            for (const { aspect, opinions } of sentence.minedOpinions) {
                console.log(`\t\tAspect text: ${aspect.text}`);
                console.log(`\t\tAspect sentiment: ${aspect.sentiment}`);
                console.log(`\t\tAspect Positive: ${aspect.confidenceScores.positive.toFixed(2)} \tNegative: ${aspect.confidenceScores.negative.toFixed(2)}`);
                console.log("\t\tAspect opinions:");
                for (const { text, sentiment, confidenceScores } of opinions) {
                    console.log(`\t\tOpinion text: ${text}`);
                    console.log(`\t\tOpinion sentiment: ${sentiment}`);
                    console.log(`\t\tOpinion Positive: ${confidenceScores.positive.toFixed(2)} \tNegative: ${confidenceScores.negative.toFixed(2)}`);
                }
            }
        });
    });
}
sentimentAnalysisWithOpinionMining(textAnalyticsClient)

Führen Sie in Ihrem Konsolenfenster Ihren Code mit node index.js aus.Run your code with node index.js in your console window.

OutputOutput

ID: 0
        Document Sentiment: positive
        Document Scores:
                Positive: 0.84  Negative: 0.16  Neutral: 0.00
        Sentences Sentiment(1):
                Sentence sentiment: positive
                Sentences Scores:
                Positive: 0.84  Negative: 0.16  Neutral: 0.00
        Mined opinions
                Aspect text: food
                Aspect sentiment: negative
                Aspect Positive: 0.01   Negative: 0.99
                Aspect opinions:
                Opinion text: unacceptable
                Opinion sentiment: negative
                Opinion Positive: 0.01  Negative: 0.99
                Aspect text: service
                Aspect sentiment: negative
                Aspect Positive: 0.01   Negative: 0.99
                Aspect opinions:
                Opinion text: unacceptable
                Opinion sentiment: negative
                Opinion Positive: 0.01  Negative: 0.99
                Aspect text: concierge
                Aspect sentiment: positive
                Aspect Positive: 1.00   Negative: 0.00
                Aspect opinions:
                Opinion text: nice
                Opinion sentiment: positive
                Opinion Positive: 1.00  Negative: 0.00

Erstellen Sie ein Zeichenfolgenarray, das das zu analysierende Dokument enthält.Create an array of strings containing the document you want to analyze. Rufen Sie die Methode analyzeSentiment() des Clients auf, und rufen Sie das zurückgegebene Objekt SentimentBatchResult ab.Call the client's analyzeSentiment() method and get the returned SentimentBatchResult object. Durchlaufen Sie die Liste mit den Ergebnissen, und geben Sie die ID des jeweiligen Dokuments sowie die Stimmung auf Dokumentebene zusammen mit Zuverlässigkeitsbewertungen aus.Iterate through the list of results, and print each document's ID, document level sentiment with confidence scores. Das Ergebnis enthält die Stimmung auf Satzebene sowie Offsets, Länge und Zuverlässigkeitsbewertungen für jedes Dokument.For each document, result contains sentence level sentiment along with offsets, length, and confidence scores.

async function sentimentAnalysis(client){

    const sentimentInput = [
        "I had the best day of my life. I wish you were there with me."
    ];
    const sentimentResult = await client.analyzeSentiment(sentimentInput);

    sentimentResult.forEach(document => {
        console.log(`ID: ${document.id}`);
        console.log(`\tDocument Sentiment: ${document.sentiment}`);
        console.log(`\tDocument Scores:`);
        console.log(`\t\tPositive: ${document.confidenceScores.positive.toFixed(2)} \tNegative: ${document.confidenceScores.negative.toFixed(2)} \tNeutral: ${document.confidenceScores.neutral.toFixed(2)}`);
        console.log(`\tSentences Sentiment(${document.sentences.length}):`);
        document.sentences.forEach(sentence => {
            console.log(`\t\tSentence sentiment: ${sentence.sentiment}`)
            console.log(`\t\tSentences Scores:`);
            console.log(`\t\tPositive: ${sentence.confidenceScores.positive.toFixed(2)} \tNegative: ${sentence.confidenceScores.negative.toFixed(2)} \tNeutral: ${sentence.confidenceScores.neutral.toFixed(2)}`);
        });
    });
}
sentimentAnalysis(textAnalyticsClient)

Führen Sie in Ihrem Konsolenfenster Ihren Code mit node index.js aus.Run your code with node index.js in your console window.

OutputOutput

ID: 0
        Document Sentiment: positive
        Document Scores:
                Positive: 1.00  Negative: 0.00  Neutral: 0.00
        Sentences Sentiment(2):
                Sentence sentiment: positive
                Sentences Scores:
                Positive: 1.00  Negative: 0.00  Neutral: 0.00
                Sentence sentiment: neutral
                Sentences Scores:
                Positive: 0.21  Negative: 0.02  Neutral: 0.77

Erstellen Sie eine Liste von Wörterbuchobjekten, die die Dokumente enthalten, die Sie analysieren möchten.Create a list of dictionary objects, containing the documents you want to analyze. Rufen Sie die Sentiment()-Methode des Clients auf, und rufen Sie das zurückgegebene SentimentBatchResult ab.Call the client's sentiment() method and get the returned SentimentBatchResult. Durchlaufen Sie die Liste der Ergebnisse, und drucken Sie die ID und den Stimmungswert jedes Dokuments.Iterate through the list of results, and print each document's ID and sentiment score. Eine gegen 0 tendierende Punktzahl deutet auf eine negative Stimmung hin, eine gegen 1 tendierende Punktzahl auf eine positive.A score closer to 0 indicates a negative sentiment, while a score closer to 1 indicates a positive sentiment.

async function sentimentAnalysis(client){

    console.log("3. This will perform sentiment analysis on the sentences.");

    const sentimentInput = {
        documents: [
            { language: "en", id: "1", text: "I had the best day of my life." },
            {
                language: "en",
                id: "2",
                text: "This was a waste of my time. The speaker put me to sleep."
            },
            {
                language: "es",
                id: "3",
                text: "No tengo dinero ni nada que dar..."
            },
            {
                language: "it",
                id: "4",
                text:
                    "L'hotel veneziano era meraviglioso. È un bellissimo pezzo di architettura."
            }
        ]
    };

    const sentimentResult = await client.analyzeSentiment(sentimentInput.documents);
    sentimentResult.forEach(document=>{
        console.log(`ID: ${document.id} Sentiment: ${document.sentiment}`);
    })
    console.log(os.EOL);
}
sentimentAnalysis(textAnalyticsClient)

Führen Sie in Ihrem Konsolenfenster Ihren Code mit node index.js aus.Run your code with node index.js in your console window.

OutputOutput

[ { id: '1', score: 0.87 } ]
[ { id: '2', score: 0.11 } ]
[ { id: '3', score: 0.44 } ]
[ { id: '4', score: 1.00 } ]

Erstellen Sie ein Zeichenfolgenarray, das das zu analysierende Dokument enthält.Create an array of strings containing the document you want to analyze. Rufen Sie die Methode detectLanguage() des Clients auf, und rufen Sie das zurückgegebene Ergebnis (DetectLanguageResultCollection) ab.Call the client's detectLanguage() method and get the returned DetectLanguageResultCollection. Durchlaufen Sie dann die Ergebnisse, und geben Sie für jedes Dokument die ID sowie die entsprechende primäre Sprache aus.Then iterate through the results, and print each document's ID with respective primary language.

async function languageDetection(client) {

    const languageInputArray = [
        "Ce document est rédigé en Français."
    ];
    const languageResult = await client.detectLanguage(languageInputArray);

    languageResult.forEach(document => {
        console.log(`ID: ${document.id}`);
        console.log(`\tPrimary Language ${document.primaryLanguage.name}`)
    });
}
languageDetection(textAnalyticsClient);

Führen Sie in Ihrem Konsolenfenster Ihren Code mit node index.js aus.Run your code with node index.js in your console window.

OutputOutput

ID: 0
        Primary Language French

Erstellen Sie ein Zeichenfolgenarray, das das zu analysierende Dokument enthält.Create an array of strings containing the document you want to analyze. Rufen Sie die Methode detectLanguage() des Clients auf, und rufen Sie das zurückgegebene Ergebnis (DetectLanguageResultCollection) ab.Call the client's detectLanguage() method and get the returned DetectLanguageResultCollection. Durchlaufen Sie dann die Ergebnisse, und geben Sie für jedes Dokument die ID sowie die entsprechende primäre Sprache aus.Then iterate through the results, and print each document's ID with respective primary language.

async function languageDetection(client) {

    const languageInputArray = [
        "Ce document est rédigé en Français."
    ];
    const languageResult = await client.detectLanguage(languageInputArray);

    languageResult.forEach(document => {
        console.log(`ID: ${document.id}`);
        console.log(`\tPrimary Language ${document.primaryLanguage.name}`)
    });
}
languageDetection(textAnalyticsClient);

Führen Sie in Ihrem Konsolenfenster Ihren Code mit node index.js aus.Run your code with node index.js in your console window.

OutputOutput

ID: 0
        Primary Language French

Erstellen Sie eine Liste mit Wörterbuchobjekten, die Ihre Dokumente enthalten.Create a list of dictionary objects containing your documents. Rufen Sie die detectLanguage()-Methode des Clients auf, und rufen Sie das zurückgegebene LanguageBatchResult ab.Call the client's detectLanguage() method and get the returned LanguageBatchResult. Durchlaufen Sie dann die Ergebnisse, und geben Sie für jedes Dokument die ID und die Sprache aus.Then iterate through the results, and print each document's ID, and language.

async function languageDetection(client) {

    console.log("1. This will detect the languages of the inputs.");
    const languageInput = {
        documents: [
            { id: "1", text: "This is a document written in English." },
            { id: "2", text: "Este es un document escrito en Español." },
            { id: "3", text: "这是一个用中文写的文件" }
        ]
    };

    const languageResult = await client.detectLanguage(languageInput.documents);
    languageResult.forEach(document => {
        console.log(`ID: ${document.id}`);
        console.log(`\tLanguage ${document.primaryLanguage.name}`)
    });
    console.log(os.EOL);
}
languageDetection(textAnalyticsClient);

Führen Sie in Ihrem Konsolenfenster Ihren Code mit node index.js aus.Run your code with node index.js in your console window.

OutputOutput

Document ID: 1 , Language: English
Document ID: 2 , Language: Spanish
Document ID: 3 , Language: Chinese_Simplified

Erstellen Sie ein Zeichenfolgenarray, das das zu analysierende Dokument enthält.Create an array of strings containing the document you want to analyze. Rufen Sie die Methode recognizeEntities() des Clients auf, und rufen Sie das Objekt RecognizeEntitiesResult ab.Call the client's recognizeEntities() method and get the RecognizeEntitiesResult object. Durchlaufen Sie die Liste mit den Ergebnissen, und geben Entitätsname, Typ, Untertyp, Offset, Länge und Bewertung aus.Iterate through the list of results, and print the entity name, type, subtype, offset, length, and score.

async function entityRecognition(client){

    const entityInputs = [
        "Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975, to develop and sell BASIC interpreters for the Altair 8800",
        "La sede principal de Microsoft se encuentra en la ciudad de Redmond, a 21 kilómetros de Seattle."
    ];
    const entityResults = await client.recognizeEntities(entityInputs);

    entityResults.forEach(document => {
        console.log(`Document ID: ${document.id}`);
        document.entities.forEach(entity => {
            console.log(`\tName: ${entity.text} \tCategory: ${entity.category} \tSubcategory: ${entity.subCategory ? entity.subCategory : "N/A"}`);
            console.log(`\tScore: ${entity.confidenceScore}`);
        });
    });
}
entityRecognition(textAnalyticsClient);

Führen Sie in Ihrem Konsolenfenster Ihren Code mit node index.js aus.Run your code with node index.js in your console window.

OutputOutput

Document ID: 0
        Name: Microsoft         Category: Organization  Subcategory: N/A
        Score: 0.29
        Name: Bill Gates        Category: Person        Subcategory: N/A
        Score: 0.78
        Name: Paul Allen        Category: Person        Subcategory: N/A
        Score: 0.82
        Name: April 4, 1975     Category: DateTime      Subcategory: Date
        Score: 0.8
        Name: 8800      Category: Quantity      Subcategory: Number
        Score: 0.8
Document ID: 1
        Name: 21        Category: Quantity      Subcategory: Number
        Score: 0.8
        Name: Seattle   Category: Location      Subcategory: GPE
        Score: 0.25

EntitätsverknüpfungEntity Linking

Erstellen Sie ein Zeichenfolgenarray, das das zu analysierende Dokument enthält.Create an array of strings containing the document you want to analyze. Rufen Sie die Methode recognizeLinkedEntities() des Clients auf, und rufen Sie das Objekt RecognizeLinkedEntitiesResult ab.Call the client's recognizeLinkedEntities() method and get the RecognizeLinkedEntitiesResult object. Durchlaufen Sie die Liste mit den Ergebnissen, und geben Entitätsname, ID, Datenquelle, URL und Treffer aus.Iterate through the list of results, and print the entity name, ID, data source, url, and matches. Jedes Objekt im Array matches enthält Offset, Länge und Bewertung für den Treffer.Every object in matches array will contain offset, length, and score for that match.

async function linkedEntityRecognition(client){

    const linkedEntityInput = [
        "Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975, to develop and sell BASIC interpreters for the Altair 8800. During his career at Microsoft, Gates held the positions of chairman, chief executive officer, president and chief software architect, while also being the largest individual shareholder until May 2014."
    ];
    const entityResults = await client.recognizeLinkedEntities(linkedEntityInput);

    entityResults.forEach(document => {
        console.log(`Document ID: ${document.id}`);
        document.entities.forEach(entity => {
            console.log(`\tName: ${entity.name} \tID: ${entity.dataSourceEntityId} \tURL: ${entity.url} \tData Source: ${entity.dataSource}`);
            console.log(`\tMatches:`)
            entity.matches.forEach(match => {
                console.log(`\t\tText: ${match.text} \tScore: ${match.confidenceScore.toFixed(2)}`);
        })
        });
    });
}
linkedEntityRecognition(textAnalyticsClient);

Führen Sie in Ihrem Konsolenfenster Ihren Code mit node index.js aus.Run your code with node index.js in your console window.

OutputOutput

Document ID: 0
        Name: Altair 8800       ID: Altair 8800         URL: https://en.wikipedia.org/wiki/Altair_8800  Data Source: Wikipedia
        Matches:
                Text: Altair 8800       Score: 0.88
        Name: Bill Gates        ID: Bill Gates  URL: https://en.wikipedia.org/wiki/Bill_Gates   Data Source: Wikipedia
        Matches:
                Text: Bill Gates        Score: 0.63
                Text: Gates     Score: 0.63
        Name: Paul Allen        ID: Paul Allen  URL: https://en.wikipedia.org/wiki/Paul_Allen   Data Source: Wikipedia
        Matches:
                Text: Paul Allen        Score: 0.60
        Name: Microsoft         ID: Microsoft   URL: https://en.wikipedia.org/wiki/Microsoft    Data Source: Wikipedia
        Matches:
                Text: Microsoft         Score: 0.55
                Text: Microsoft         Score: 0.55
        Name: April 4   ID: April 4     URL: https://en.wikipedia.org/wiki/April_4      Data Source: Wikipedia
        Matches:
                Text: April 4   Score: 0.32
        Name: BASIC     ID: BASIC       URL: https://en.wikipedia.org/wiki/BASIC        Data Source: Wikipedia
        Matches:
                Text: BASIC     Score: 0.33

Erkennung von personenbezogenen Informationen (Personally Identifying Information, PII)Personally Identifying Information (PII) Recognition

Erstellen Sie ein Zeichenfolgenarray, das das zu analysierende Dokument enthält.Create an array of strings containing the document you want to analyze. Rufen Sie die Methode recognizePiiEntities() des Clients auf, und rufen Sie das Objekt RecognizePIIEntitiesResult ab.Call the client's recognizePiiEntities() method and get the RecognizePIIEntitiesResult object. Durchlaufen Sie die Liste mit den Ergebnissen, und geben Entitätsname, Typ und Bewertung aus.Iterate through the list of results, and print the entity name, type, and score.

async function piiRecognition(client) {

    const documents = [
        "The employee's phone number is (555) 555-5555."
    ];

    const results = await client.recognizePiiEntities(documents, "en");
    for (const result of results) {
        if (result.error === undefined) {
            console.log("Redacted Text: ", result.redactedText);
            console.log(" -- Recognized PII entities for input", result.id, "--");
            for (const entity of result.entities) {
                console.log(entity.text, ":", entity.category, "(Score:", entity.confidenceScore, ")");
            }
        } else {
            console.error("Encountered an error:", result.error);
        }
    }
}
piiRecognition(textAnalyticsClient)

Führen Sie in Ihrem Konsolenfenster Ihren Code mit node index.js aus.Run your code with node index.js in your console window.

OutputOutput

Redacted Text:  The employee's phone number is **************.
 -- Recognized PII entities for input 0 --
(555) 555-5555 : Phone Number (Score: 0.8 )

Erstellen Sie ein Zeichenfolgenarray, das das zu analysierende Dokument enthält.Create an array of strings containing the document you want to analyze. Rufen Sie die Methode recognizeEntities() des Clients auf, und rufen Sie das Objekt RecognizeEntitiesResult ab.Call the client's recognizeEntities() method and get the RecognizeEntitiesResult object. Durchlaufen Sie die Liste mit den Ergebnissen, und geben Entitätsname, Typ, Untertyp, Offset, Länge und Bewertung aus.Iterate through the list of results, and print the entity name, type, subtype, offset, length, and score.

async function entityRecognition(client){

    const entityInputs = [
        "Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975, to develop and sell BASIC interpreters for the Altair 8800",
        "La sede principal de Microsoft se encuentra en la ciudad de Redmond, a 21 kilómetros de Seattle."
    ];
    const entityResults = await client.recognizeEntities(entityInputs);

    entityResults.forEach(document => {
        console.log(`Document ID: ${document.id}`);
        document.entities.forEach(entity => {
            console.log(`\tName: ${entity.text} \tCategory: ${entity.category} \tSubcategory: ${entity.subCategory ? entity.subCategory : "N/A"}`);
            console.log(`\tScore: ${entity.confidenceScore}`);
        });
    });
}
entityRecognition(textAnalyticsClient);

Führen Sie in Ihrem Konsolenfenster Ihren Code mit node index.js aus.Run your code with node index.js in your console window.

OutputOutput

Document ID: 0
        Name: Microsoft         Category: Organization  Subcategory: N/A
        Score: 0.29
        Name: Bill Gates        Category: Person        Subcategory: N/A
        Score: 0.78
        Name: Paul Allen        Category: Person        Subcategory: N/A
        Score: 0.82
        Name: April 4, 1975     Category: DateTime      Subcategory: Date
        Score: 0.8
        Name: 8800      Category: Quantity      Subcategory: Number
        Score: 0.8
Document ID: 1
        Name: 21        Category: Quantity      Subcategory: Number
        Score: 0.8
        Name: Seattle   Category: Location      Subcategory: GPE
        Score: 0.25

EntitätsverknüpfungEntity Linking

Erstellen Sie ein Zeichenfolgenarray, das das zu analysierende Dokument enthält.Create an array of strings containing the document you want to analyze. Rufen Sie die Methode recognizeLinkedEntities() des Clients auf, und rufen Sie das Objekt RecognizeLinkedEntitiesResult ab.Call the client's recognizeLinkedEntities() method and get the RecognizeLinkedEntitiesResult object. Durchlaufen Sie die Liste mit den Ergebnissen, und geben Entitätsname, ID, Datenquelle, URL und Treffer aus.Iterate through the list of results, and print the entity name, ID, data source, url, and matches. Jedes Objekt im Array matches enthält Offset, Länge und Bewertung für den Treffer.Every object in matches array will contain offset, length, and score for that match.

async function linkedEntityRecognition(client){

    const linkedEntityInput = [
        "Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975, to develop and sell BASIC interpreters for the Altair 8800. During his career at Microsoft, Gates held the positions of chairman, chief executive officer, president and chief software architect, while also being the largest individual shareholder until May 2014."
    ];
    const entityResults = await client.recognizeLinkedEntities(linkedEntityInput);

    entityResults.forEach(document => {
        console.log(`Document ID: ${document.id}`);
        document.entities.forEach(entity => {
            console.log(`\tName: ${entity.name} \tID: ${entity.dataSourceEntityId} \tURL: ${entity.url} \tData Source: ${entity.dataSource}`);
            console.log(`\tMatches:`)
            entity.matches.forEach(match => {
                console.log(`\t\tText: ${match.text} \tScore: ${match.confidenceScore.toFixed(2)}`);
        })
        });
    });
}
linkedEntityRecognition(textAnalyticsClient);

Führen Sie in Ihrem Konsolenfenster Ihren Code mit node index.js aus.Run your code with node index.js in your console window.

OutputOutput

Document ID: 0
        Name: Altair 8800       ID: Altair 8800         URL: https://en.wikipedia.org/wiki/Altair_8800  Data Source: Wikipedia
        Matches:
                Text: Altair 8800       Score: 0.88
        Name: Bill Gates        ID: Bill Gates  URL: https://en.wikipedia.org/wiki/Bill_Gates   Data Source: Wikipedia
        Matches:
                Text: Bill Gates        Score: 0.63
                Text: Gates     Score: 0.63
        Name: Paul Allen        ID: Paul Allen  URL: https://en.wikipedia.org/wiki/Paul_Allen   Data Source: Wikipedia
        Matches:
                Text: Paul Allen        Score: 0.60
        Name: Microsoft         ID: Microsoft   URL: https://en.wikipedia.org/wiki/Microsoft    Data Source: Wikipedia
        Matches:
                Text: Microsoft         Score: 0.55
                Text: Microsoft         Score: 0.55
        Name: April 4   ID: April 4     URL: https://en.wikipedia.org/wiki/April_4      Data Source: Wikipedia
        Matches:
                Text: April 4   Score: 0.32
        Name: BASIC     ID: BASIC       URL: https://en.wikipedia.org/wiki/BASIC        Data Source: Wikipedia
        Matches:
                Text: BASIC     Score: 0.33

Erstellen Sie eine Liste mit Objekten, die Ihre Dokumente enthält.Create a list of objects, containing your documents. Rufen Sie die entities()-Methode des Clients auf, und rufen Sie das EntitiesBatchResult-Objekt ab.Call the client's entities() method and get the EntitiesBatchResult object. Durchlaufen Sie die Liste der Ergebnisse, und geben Sie die ID jedes Dokuments aus.Iterate through the list of results, and print each document's ID. Drucken Sie für jede erkannte Entität ihren Wikipedia-Namen, den Typ und Untertypen (falls vorhanden) sowie die Fundstellen im Originaltext.For each detected entity, print its wikipedia name, the type and sub-types (if exists) as well as the locations in the original text.

async function entityRecognition(client){
    console.log("3. This will perform Entity recognition on the sentences.");

    const entityInputs = {
        documents: [
            {
                language: "en",
                id: "1",
                text:
                    "Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975, to develop and sell BASIC interpreters for the Altair 8800"
            },
            {
                language: "es",
                id: "2",
                text:
                    "La sede principal de Microsoft se encuentra en la ciudad de Redmond, a 21 kilómetros de Seattle."
            }
        ]
    };

    const entityResults = await client.recognizeEntities(entityInputs.documents);
    entityResults.forEach(document => {
        console.log(`Document ID: ${document.id}`);
        for (const e of document.entities) {
            console.log(`\tName: ${e.text} Category: ${e.category} Sub Category: ${e.subCategory}`);
            console.log(`\t\tOffset: ${e.offset} Length: ${e.length} Score: ${e.confidenceScore}`);
        }
    });

    console.log(os.EOL);
}
entityRecognition(textAnalyticsClient);

Führen Sie in Ihrem Konsolenfenster Ihren Code mit node index.js aus.Run your code with node index.js in your console window.

OutputOutput

Document ID: 1
    Name: Microsoft,        Type: Organization,     Sub-Type: N/A
    Offset: 0, Length: 9,   Score: 1.0
    Name: Bill Gates,       Type: Person,   Sub-Type: N/A
    Offset: 25, Length: 10, Score: 0.999847412109375
    Name: Paul Allen,       Type: Person,   Sub-Type: N/A
    Offset: 40, Length: 10, Score: 0.9988409876823425
    Name: April 4,  Type: Other,    Sub-Type: N/A
    Offset: 54, Length: 7,  Score: 0.8
    Name: April 4, 1975,    Type: DateTime, Sub-Type: Date
    Offset: 54, Length: 13, Score: 0.8
    Name: BASIC,    Type: Other,    Sub-Type: N/A
    Offset: 89, Length: 5,  Score: 0.8
    Name: Altair 8800,      Type: Other,    Sub-Type: N/A
    Offset: 116, Length: 11,        Score: 0.8

Document ID: 2
    Name: Microsoft,        Type: Organization,     Sub-Type: N/A
    Offset: 21, Length: 9,  Score: 0.999755859375
    Name: Redmond (Washington),     Type: Location, Sub-Type: N/A
    Offset: 60, Length: 7,  Score: 0.9911284446716309
    Name: 21 kilómetros,    Type: Quantity, Sub-Type: Dimension
    Offset: 71, Length: 13, Score: 0.8
    Name: Seattle,  Type: Location, Sub-Type: N/A
    Offset: 88, Length: 7,  Score: 0.9998779296875

Erstellen Sie ein Zeichenfolgenarray, das das zu analysierende Dokument enthält.Create an array of strings containing the document you want to analyze. Rufen Sie die Methode extractKeyPhrases() des Clients auf, und rufen Sie das zurückgegebene Objekt ExtractKeyPhrasesResult ab.Call the client's extractKeyPhrases() method and get the returned ExtractKeyPhrasesResult object. Durchlaufen Sie die Ergebnisse, und geben Sie die ID und alle erkannten Schlüsselbegriffe aus.Iterate through the results and print each document's ID, and any detected key phrases.

async function keyPhraseExtraction(client){

    const keyPhrasesInput = [
        "My cat might need to see a veterinarian.",
    ];
    const keyPhraseResult = await client.extractKeyPhrases(keyPhrasesInput);
    
    keyPhraseResult.forEach(document => {
        console.log(`ID: ${document.id}`);
        console.log(`\tDocument Key Phrases: ${document.keyPhrases}`);
    });
}
keyPhraseExtraction(textAnalyticsClient);

Führen Sie in Ihrem Konsolenfenster Ihren Code mit node index.js aus.Run your code with node index.js in your console window.

OutputOutput

ID: 0
        Document Key Phrases: cat,veterinarian

Erstellen Sie ein Zeichenfolgenarray, das das zu analysierende Dokument enthält.Create an array of strings containing the document you want to analyze. Rufen Sie die Methode extractKeyPhrases() des Clients auf, und rufen Sie das zurückgegebene Objekt ExtractKeyPhrasesResult ab.Call the client's extractKeyPhrases() method and get the returned ExtractKeyPhrasesResult object. Durchlaufen Sie die Ergebnisse, und geben Sie die ID und alle erkannten Schlüsselbegriffe aus.Iterate through the results and print each document's ID, and any detected key phrases.

async function keyPhraseExtraction(client){

    const keyPhrasesInput = [
        "My cat might need to see a veterinarian.",
    ];
    const keyPhraseResult = await client.extractKeyPhrases(keyPhrasesInput);
    
    keyPhraseResult.forEach(document => {
        console.log(`ID: ${document.id}`);
        console.log(`\tDocument Key Phrases: ${document.keyPhrases}`);
    });
}
keyPhraseExtraction(textAnalyticsClient);

Führen Sie in Ihrem Konsolenfenster Ihren Code mit node index.js aus.Run your code with node index.js in your console window.

OutputOutput

ID: 0
        Document Key Phrases: cat,veterinarian

Erstellen Sie eine Liste mit Objekten, die Ihre Dokumente enthält.Create a list of objects, containing your documents. Rufen Sie die keyPhrases()-Methode des Clients auf, und rufen Sie das zurückgegebene KeyPhraseBatchResult-Objekt ab.Call the client's keyPhrases() method and get the returned KeyPhraseBatchResult object. Durchlaufen Sie die Ergebnisse, und geben Sie die ID und alle erkannten Schlüsselbegriffe aus.Iterate through the results and print each document's ID, and any detected key phrases.

async function keyPhraseExtraction(client){

    console.log("2. This will extract key phrases from the sentences.");
    const keyPhrasesInput = {
        documents: [
            { language: "ja", id: "1", text: "猫は幸せ" },
            {
                language: "de",
                id: "2",
                text: "Fahrt nach Stuttgart und dann zum Hotel zu Fu."
            },
            {
                language: "en",
                id: "3",
                text: "My cat might need to see a veterinarian."
            },
            { language: "es", id: "4", text: "A mi me encanta el fútbol!" }
        ]
    };

    const keyPhraseResult = await client.extractKeyPhrases(keyPhrasesInput.documents, 'en');
    keyPhraseResult.forEach(document=>{
        console.log(`ID: ${document.id} KeyPhrases: ${document.keyPhrases}`)
    })
    console.log(os.EOL);
}
keyPhraseExtraction(textAnalyticsClient);

Führen Sie in Ihrem Konsolenfenster Ihren Code mit node index.js aus.Run your code with node index.js in your console window.

OutputOutput

[
    { id: '1', keyPhrases: [ '幸せ' ] }
    { id: '2', keyPhrases: [ 'Stuttgart', "hotel", "Fahrt", "Fu" ] }
    { id: '3', keyPhrases: [ 'cat', 'veterinarian' ] }
    { id: '3', keyPhrases: [ 'fútbol' ] }
]

Erstellen Sie die neue Funktion analyze_example(), die die Funktion beginAnalyze() aufruft.Create a new function called analyze_example(), which calls the beginAnalyze() function. Daraus resultiert ein zeitintensiver Vorgang, aus dem Ergebnisse abgerufen werden.The result will be a long running operation which will be polled for results.

const documents = [
  "Microsoft was founded by Bill Gates and Paul Allen.",
];

async function analyze_example(client) {
  console.log("== Analyze Sample ==");

  const tasks = {
    entityRecognitionTasks: [{ modelVersion: "latest" }]
  };
  const poller = await client.beginAnalyze(documents, tasks);
  const resultPages = await poller.pollUntilDone();

  for await (const page of resultPages) {
    const entitiesResults = page.entitiesRecognitionResults![0];
    for (const doc of entitiesResults) {
      console.log(`- Document ${doc.id}`);
      if (!doc.error) {
        console.log("\tEntities:");
        for (const entity of doc.entities) {
          console.log(`\t- Entity ${entity.text} of type ${entity.category}`);
        }
      } else {
        console.error("  Error:", doc.error);
      }
    }
  }
}

analyze_example(textAnalyticsClient);

AusgabeOutput

== Analyze Sample ==
- Document 0
        Entities:
        - Entity Microsoft of type Organization
        - Entity Bill Gates of type Person
        - Entity Paul Allen of type Person

Sie können den Analyze-Vorgang auch verwenden, um personenbezogene Informationen zu erkennen und Schlüsselbegriffe zu extrahieren.You can also use the Analyze operation to detect PII and key phrase extraction. Die Analyze-Beispiele für JavaScript und TypeScript finden Sie auf GitHub.See the Analyze samples for JavaScript and TypeScript on GitHub.

Dieses Feature ist in Version 3.0 nicht verfügbar.This feature is not available in version 3.0.

Dieses Feature ist in Version 2.1 nicht verfügbar.This feature is not available in version 2.1.

v3.1-Referenzdokumentation | v3.1-Bibliotheksquellcode | v3.1-Paket (PiPy) | v3.1-Beispielev3.1 Reference documentation | v3.1 Library source code | v3.1 Package (PiPy) | v3.1 Samples

v3-Referenzdokumentation | Quellcode der v3-Bibliothek | v3-Paket (PiPy) | v3-Beispielev3 Reference documentation | v3 Library source code | v3 Package (PiPy) | v3 Samples

v2-Referenzdokumentation | Quellcode der v2-Bibliothek | v2-Paket (PiPy) | v2-Beispielev2 Reference documentation | v2 Library source code | v2 Package (PiPy) | v2 Samples

pip install azure-ai-textanalytics --pre

pip install --upgrade azure-ai-textanalytics

pip install --upgrade azure-cognitiveservices-language-textanalytics

Der Textanalyseclient ist ein Objekt vom Typ TextAnalyticsClient, das sich bei Azure authentifiziert.The Text Analytics client is a TextAnalyticsClient object that authenticates to Azure. Der Client bietet verschiedene Methoden, um Text zu analysieren.The client provides several methods for analyzing text.

Bei der Verarbeitung wird Text an die API in Form einer Liste von documents gesendet, die ihrerseits entweder eine Zeichenfolgenliste, eine Liste in einer wörterbuchähnlichen Darstellung oder eine Liste von TextDocumentInput/DetectLanguageInput sind.When processing text is sent to the API as a list of documents, which is either as a list of string, a list of dict-like representation, or as a list of TextDocumentInput/DetectLanguageInput. Ein dict-like-Objekt enthält eine Kombination aus id, text und language/country_hint.A dict-like object contains a combination of id, text, and language/country_hint. Das text-Attribut speichert den zu analysierenden Text in der Ausgangssprache (country_hint), und id kann ein beliebiger Wert sein.The text attribute stores the text to be analyzed in the origin country_hint, and the id can be any value.

Das Antwortobjekt ist eine Liste mit den Analyseinformationen für jedes Dokument.The response object is a list containing the analysis information for each document.

Der Textanalyseclient ist ein Objekt vom Typ TextAnalyticsClient, das sich bei Azure mit Ihrem Schlüssel authentifiziert.The Text Analytics client is a TextAnalyticsClient object that authenticates to Azure using your key. Der Client bietet verschiedene Methoden, um Text als Batch zu analysieren.The client provides several methods for analyzing text as a batch.

Bei der Batchverarbeitung wird Text als Liste vom Typ documents an die API gesendet. Hierbei handelt es sich um Objekte vom Typ dictionary, die je nach verwendeter Methode eine Kombination aus den Attributen id, text und language enthalten.When batch processing text is sent to the API as a list of documents, which are dictionary objects containing a combination of id, text, and language attributes depending on the method used. Das text-Attribut speichert den zu analysierenden Text in der Ausgangssprache (language), und id kann ein beliebiger Wert sein.The text attribute stores the text to be analyzed in the origin language, and the id can be any value.

Das Antwortobjekt ist eine Liste mit den analysierten Informationen für jedes Dokument.The response object is a list containing the analyzed information for each document.

Der Textanalyse-Client ist ein TextAnalyticsClient-Objekt, das sich mit Ihrem Schlüssel bei Azure authentifiziert.The Text Analytics client is a TextAnalyticsClient object that authenticates to Azure using your key. Der Client verfügt über verschiedene Methoden zum Analysieren von Text als einzelne Zeichenfolge oder als Batch.The client provides several methods for analyzing text, as a single string, or a batch.

Text wird als documents-Liste an die API gesendet. Hierbei handelt es sich um dictionary-Objekte, die je nach verwendeter Methode eine Kombination aus den Attributen id, text und language enthalten.Text is sent to the API as a list of documents, which are dictionary objects containing a combination of id, text, and language attributes depending on the method used. Das text-Attribut speichert den zu analysierenden Text in der Ausgangssprache (language), und id kann ein beliebiger Wert sein.The text attribute stores the text to be analyzed in the origin language, and the id can be any value.

• Authentifizieren des ClientsAuthenticate the client
• StandpunktanalyseSentiment Analysis
• Opinion MiningOpinion mining
• SprachenerkennungLanguage detection
• Erkennung benannter EntitätenNamed Entity recognition
• Erkennung von personenbezogenen InformationenPersonally Identifiable Information recognition
• EntitätsverknüpfungEntity linking
• SchlüsselbegriffserkennungKey phrase extraction

• Authentifizieren des ClientsAuthenticate the client
• StandpunktanalyseSentiment Analysis
• SprachenerkennungLanguage detection
• Erkennung benannter EntitätenNamed Entity recognition
• EntitätsverknüpfungEntity linking
• SchlüsselbegriffserkennungKey phrase extraction

• Authentifizieren des ClientsAuthenticate the client
• StandpunktanalyseSentiment Analysis
• SprachenerkennungLanguage detection
• Erkennung benannter EntitätenNamed Entity recognition
• EntitätsverknüpfungEntity linking
• SchlüsselbegriffserkennungKey phrase extraction

Erstellen Sie eine Funktion, um das Objekt TextAnalyticsClient mit dem oben erstellten Schlüssel (key) und Endpunkt (endpoint) zu instanziieren.Create a function to instantiate the TextAnalyticsClient object with your key AND endpoint created above. Erstellen Sie anschließend einen neuen Client.Then create a new client.

from azure.ai.textanalytics import TextAnalyticsClient
from azure.core.credentials import AzureKeyCredential

def authenticate_client():
    ta_credential = AzureKeyCredential(key)
    text_analytics_client = TextAnalyticsClient(
            endpoint=endpoint, 
            credential=ta_credential)
    return text_analytics_client

client = authenticate_client()

Erstellen Sie eine Funktion, um das Objekt TextAnalyticsClient mit dem oben erstellten Schlüssel (key) und Endpunkt (endpoint) zu instanziieren.Create a function to instantiate the TextAnalyticsClient object with your key AND endpoint created above. Erstellen Sie anschließend einen neuen Client.Then create a new client. Beachten Sie, dass api_version=TextAnalyticsApiVersion.V3_0 für die Verwendung von Version 3.0 definiert sein muss.Note that api_version=TextAnalyticsApiVersion.V3_0 should be defined for using version 3.0.

# use this code if you're using SDK version is 5.0.0
from azure.ai.textanalytics import TextAnalyticsClient
from azure.core.credentials import AzureKeyCredential

def authenticate_client():
    ta_credential = AzureKeyCredential(key)
    text_analytics_client = TextAnalyticsClient(
            endpoint=endpoint, 
            credential=ta_credential) 
    return text_analytics_client

client = authenticate_client()

Wenn Sie Version 5.1.0 der Clientbibliothek mit pip install azure-ai-textanalytics --pre installiert haben, können Sie Version 3.0 der Textanalyse-API mit dem Parameter api_version des Clients angeben.If you installed v5.1.0 of the client library using pip install azure-ai-textanalytics --pre, you can specify v3.0 of the Text Analytics API with the client's api_version parameter. Verwenden Sie die folgende authenticate_client()-Methode nur, wenn Ihr Client Version 5.1.0 oder höher aufweist.Only use the following authenticate_client() method if your client is v5.1.0 or later.

# Only use the following code sample if you're using v5.1.0 of the client library, 
# and are looking to specify v3.0 of the Text Analytics API for your client
from azure.ai.textanalytics import TextAnalyticsClient, TextAnalyticsApiVersion
from azure.core.credentials import AzureKeyCredential
def authenticate_client():
   ta_credential = AzureKeyCredential(key)
   text_analytics_client = TextAnalyticsClient(
     endpoint=endpoint,
     credential=ta_credential,
     api_version=TextAnalyticsApiVersion.V3_0
   )
   
client = authenticate_client()

# -*- coding: utf-8 -*-

import os
from azure.ai.textanalytics import TextAnalyticsClient
from azure.core.credentials import AzureKeyCredential

Erstellen Sie eine Funktion, um das Objekt TextAnalyticsClient mit dem oben erstellten Schlüssel (key) und Endpunkt (endpoint) zu instanziieren.Create a function to instantiate the TextAnalyticsClient object with your key AND endpoint created above. Erstellen Sie anschließend einen neuen Client.Then create a new client.

def authenticateClient():
    credential = AzureKeyCredential(key)
    text_analytics_client = TextAnalyticsClient(
        endpoint=endpoint, credential=credential)
    return text_analytics_client

Erstellen Sie eine neue Funktion namens sentiment_analysis_example(), die den Client als Argument akzeptiert und anschließend die Funktion analyze_sentiment() aufruft.Create a new function called sentiment_analysis_example() that takes the client as an argument, then calls the analyze_sentiment() function. Das zurückgegebene Antwortobjekt enthält die Stimmungsbezeichnung und den Stimmungswert des gesamten Eingabedokuments sowie eine Standpunktanalyse für jeden Satz.The returned response object will contain the sentiment label and score of the entire input document, as well as a sentiment analysis for each sentence.

def sentiment_analysis_example(client):

    documents = ["I had the best day of my life. I wish you were there with me."]
    response = client.analyze_sentiment(documents=documents)[0]
    print("Document Sentiment: {}".format(response.sentiment))
    print("Overall scores: positive={0:.2f}; neutral={1:.2f}; negative={2:.2f} \n".format(
        response.confidence_scores.positive,
        response.confidence_scores.neutral,
        response.confidence_scores.negative,
    ))
    for idx, sentence in enumerate(response.sentences):
        print("Sentence: {}".format(sentence.text))
        print("Sentence {} sentiment: {}".format(idx+1, sentence.sentiment))
        print("Sentence score:\nPositive={0:.2f}\nNeutral={1:.2f}\nNegative={2:.2f}\n".format(
            sentence.confidence_scores.positive,
            sentence.confidence_scores.neutral,
            sentence.confidence_scores.negative,
        ))
          
sentiment_analysis_example(client)

OutputOutput

Document Sentiment: positive
Overall scores: positive=1.00; neutral=0.00; negative=0.00 

Sentence: I had the best day of my life.
Sentence 1 sentiment: positive
Sentence score:
Positive=1.00
Neutral=0.00
Negative=0.00

Sentence: I wish you were there with me.
Sentence 2 sentiment: neutral
Sentence score:
Positive=0.21
Neutral=0.77
Negative=0.02

Opinion MiningOpinion mining

Zum Durchführen von Stimmungsanalyse mit Opinion Mining erstellen Sie eine neue Funktion mit dem Namen sentiment_analysis_with_opinion_mining_example(), die den Client als Argument annimmt und dann die Funktion analyze_sentiment() mit dem Optionsflag show_opinion_mining=True aufruft.In order to do sentiment analysis with opinion mining, create a new function called sentiment_analysis_with_opinion_mining_example() that takes the client as an argument, then calls the analyze_sentiment() function with option flag show_opinion_mining=True. Das zurückgegebene Antwortobjekt enthält nicht nur die Stimmungsbezeichnung und den Stimmungswert des gesamten Eingabedokuments mit einer Stimmungsanalyse für jeden Satz, sondern außerdem die Standpunktanalyse auf Ansichts- und Meinungsebene.The returned response object will contain not only the sentiment label and score of the entire input document with sentiment analysis for each sentence, but also aspect and opinion level sentiment analysis.

def sentiment_analysis_with_opinion_mining_example(client):

    documents = [
        "The food and service were unacceptable, but the concierge were nice"
    ]

    result = client.analyze_sentiment(documents, show_opinion_mining=True)
    doc_result = [doc for doc in result if not doc.is_error]

    positive_reviews = [doc for doc in doc_result if doc.sentiment == "positive"]
    negative_reviews = [doc for doc in doc_result if doc.sentiment == "negative"]

    positive_mined_opinions = []
    mixed_mined_opinions = []
    negative_mined_opinions = []

    for document in doc_result:
        print("Document Sentiment: {}".format(document.sentiment))
        print("Overall scores: positive={0:.2f}; neutral={1:.2f}; negative={2:.2f} \n".format(
            document.confidence_scores.positive,
            document.confidence_scores.neutral,
            document.confidence_scores.negative,
        ))
        for sentence in document.sentences:
            print("Sentence: {}".format(sentence.text))
            print("Sentence sentiment: {}".format(sentence.sentiment))
            print("Sentence score:\nPositive={0:.2f}\nNeutral={1:.2f}\nNegative={2:.2f}\n".format(
                sentence.confidence_scores.positive,
                sentence.confidence_scores.neutral,
                sentence.confidence_scores.negative,
            ))
            for mined_opinion in sentence.mined_opinions:
                aspect = mined_opinion.aspect
                print("......'{}' aspect '{}'".format(aspect.sentiment, aspect.text))
                print("......Aspect score:\n......Positive={0:.2f}\n......Negative={1:.2f}\n".format(
                    aspect.confidence_scores.positive,
                    aspect.confidence_scores.negative,
                ))
                for opinion in mined_opinion.opinions:
                    print("......'{}' opinion '{}'".format(opinion.sentiment, opinion.text))
                    print("......Opinion score:\n......Positive={0:.2f}\n......Negative={1:.2f}\n".format(
                        opinion.confidence_scores.positive,
                        opinion.confidence_scores.negative,
                    ))
            print("\n")
        print("\n")
          
sentiment_analysis_with_opinion_mining_example(client)

OutputOutput

Document Sentiment: positive
Overall scores: positive=0.84; neutral=0.00; negative=0.16

Sentence: The food and service were unacceptable, but the concierge were nice
Sentence sentiment: positive
Sentence score:
Positive=0.84
Neutral=0.00
Negative=0.16

......'negative' aspect 'food'
......Aspect score:
......Positive=0.01
......Negative=0.99

......'negative' opinion 'unacceptable'
......Opinion score:
......Positive=0.01
......Negative=0.99

......'negative' aspect 'service'
......Aspect score:
......Positive=0.01
......Negative=0.99

......'negative' opinion 'unacceptable'
......Opinion score:
......Positive=0.01
......Negative=0.99

......'positive' aspect 'concierge'
......Aspect score:
......Positive=1.00
......Negative=0.00

......'positive' opinion 'nice'
......Opinion score:
......Positive=1.00
......Negative=0.00





Press any key to continue . . .

Erstellen Sie eine neue Funktion namens sentiment_analysis_example(), die den Client als Argument akzeptiert und anschließend die Funktion analyze_sentiment() aufruft.Create a new function called sentiment_analysis_example() that takes the client as an argument, then calls the analyze_sentiment() function. Das zurückgegebene Antwortobjekt enthält die Stimmungsbezeichnung und den Stimmungswert des gesamten Eingabedokuments sowie eine Standpunktanalyse für jeden Satz.The returned response object will contain the sentiment label and score of the entire input document, as well as a sentiment analysis for each sentence.

def sentiment_analysis_example(client):

    documents = ["I had the best day of my life. I wish you were there with me."]
    response = client.analyze_sentiment(documents = documents)[0]
    print("Document Sentiment: {}".format(response.sentiment))
    print("Overall scores: positive={0:.2f}; neutral={1:.2f}; negative={2:.2f} \n".format(
        response.confidence_scores.positive,
        response.confidence_scores.neutral,
        response.confidence_scores.negative,
    ))
    for idx, sentence in enumerate(response.sentences):
        print("Sentence: {}".format(sentence.text))
        print("Sentence {} sentiment: {}".format(idx+1, sentence.sentiment))
        print("Sentence score:\nPositive={0:.2f}\nNeutral={1:.2f}\nNegative={2:.2f}\n".format(
            sentence.confidence_scores.positive,
            sentence.confidence_scores.neutral,
            sentence.confidence_scores.negative,
        ))
          
sentiment_analysis_example(client)

OutputOutput

Document Sentiment: positive
Overall scores: positive=1.00; neutral=0.00; negative=0.00 

Sentence: I had the best day of my life.
Sentence 1 sentiment: positive
Sentence score:
Positive=1.00
Neutral=0.00
Negative=0.00

Sentence: I wish you were there with me.
Sentence 2 sentiment: neutral
Sentence score:
Positive=0.21
Neutral=0.77
Negative=0.02

Authentifizieren Sie ein Clientobjekt, und rufen Sie die Funktion sentiment() auf.Authenticate a client object, and call the sentiment() function. Durchlaufen Sie die Ergebnisse, und drucken Sie die ID und den Stimmungswert jedes Dokuments.Iterate through the results, and print each document's ID, and sentiment score. Eine gegen 0 tendierende Punktzahl deutet auf eine negative Stimmung hin, eine gegen 1 tendierende Punktzahl auf eine positive.A score closer to 0 indicates a negative sentiment, while a score closer to 1 indicates a positive sentiment.

def sentiment():
    
    client = authenticateClient()

    try:
        documents = [
            {"id": "1", "language": "en", "text": "I had the best day of my life."},
            {"id": "2", "language": "en",
                "text": "This was a waste of my time. The speaker put me to sleep."},
            {"id": "3", "language": "es", "text": "No tengo dinero ni nada que dar..."},
            {"id": "4", "language": "it",
                "text": "L'hotel veneziano era meraviglioso. È un bellissimo pezzo di architettura."}
        ]

        response = client.analyze_sentiment(documents=documents)
        for document in response:
            print("Document Id: ", document.id, ", Sentiment: ", document.sentiment)
            score = document.confidence_scores
            print("\tConfidence Scores: ")
            print("\t\tNegative: {:.2f}\tNeutral: {:.2f}\tPositive: {:.2f}"
                  .format(score.negative, score.neutral, score.positive))

    except Exception as err:
        print("Encountered exception. {}".format(err))
sentiment()

OutputOutput

Document ID: 1 , Sentiment Score: 0.87
Document ID: 2 , Sentiment Score: 0.11
Document ID: 3 , Sentiment Score: 0.44
Document ID: 4 , Sentiment Score: 1.00

Erstellen Sie eine neue Funktion namens language_detection_example(), die den Client als Argument akzeptiert und anschließend die Funktion detect_language() aufruft.Create a new function called language_detection_example() that takes the client as an argument, then calls the detect_language() function. Das zurückgegebene Antwortobjekt enthält die erkannte Sprache in primary_language (sofern erfolgreich). Andernfalls enthält es eine Fehlermeldung (error).The returned response object will contain the detected language in primary_language if successful, and an error if not.

def language_detection_example(client):
    try:
        documents = ["Ce document est rédigé en Français."]
        response = client.detect_language(documents = documents, country_hint = 'us')[0]
        print("Language: ", response.primary_language.name)

    except Exception as err:
        print("Encountered exception. {}".format(err))
language_detection_example(client)

OutputOutput

Language:  French

Erstellen Sie eine neue Funktion namens language_detection_example(), die den Client als Argument akzeptiert und anschließend die Funktion detect_language() aufruft.Create a new function called language_detection_example() that takes the client as an argument, then calls the detect_language() function. Das zurückgegebene Antwortobjekt enthält die erkannte Sprache in primary_language (sofern erfolgreich). Andernfalls enthält es eine Fehlermeldung (error).The returned response object will contain the detected language in primary_language if successful, and an error if not.

def language_detection_example(client):
    try:
        documents = ["Ce document est rédigé en Français."]
        response = client.detect_language(documents = documents, country_hint = 'us')[0]
        print("Language: ", response.primary_language.name)

    except Exception as err:
        print("Encountered exception. {}".format(err))
language_detection_example(client)

OutputOutput

Language:  French

Rufen Sie mit dem zuvor erstellten Client die Funktion detect_language() auf, um das Ergebnis abzurufen.Using the client created earlier, call detect_language() and get the result. Durchlaufen Sie dann die Ergebnisse, und drucken Sie die ID und die erste zurückgegebene Sprache.Then iterate through the results, and print each document's ID, and the first returned language.

def language_detection():
    client = authenticateClient()

    try:
        documents = [
            {'id': '1', 'text': 'This is a document written in English.'},
            {'id': '2', 'text': 'Este es un document escrito en Español.'},
            {'id': '3', 'text': '这是一个用中文写的文件'}
        ]
        response = client.detect_language(documents=documents)

        for document in response:
            print("Document Id: ", document.id, ", Language: ",
                  document.primary_language.name)

    except Exception as err:
        print("Encountered exception. {}".format(err))
language_detection()

OutputOutput

Document ID: 1 , Language: English
Document ID: 2 , Language: Spanish
Document ID: 3 , Language: Chinese_Simplified

Erstellen Sie eine neue Funktion namens entity_recognition_example, die den Client als Argument akzeptiert und anschließend die Funktion recognize_entities() aufruft und die Ergebnisse durchläuft.Create a new function called entity_recognition_example that takes the client as an argument, then calls the recognize_entities() function and iterates through the results. Das zurückgegebene Antwortobjekt enthält die Liste erkannter Entitäten in entity (sofern erfolgreich). Andernfalls enthält es eine Fehlermeldung (error).The returned response object will contain the list of detected entities in entity if successful, and an error if not. Geben Sie für die erkannten Entitäten jeweils die Kategorie und Unterkategorie aus (sofern vorhanden).For each detected entity, print its Category and Sub-Category if exists.

def entity_recognition_example(client):

    try:
        documents = ["I had a wonderful trip to Seattle last week."]
        result = client.recognize_entities(documents = documents)[0]

        print("Named Entities:\n")
        for entity in result.entities:
            print("\tText: \t", entity.text, "\tCategory: \t", entity.category, "\tSubCategory: \t", entity.subcategory,
                    "\n\tConfidence Score: \t", round(entity.confidence_score, 2), "\tLength: \t", entity.length, "\tOffset: \t", entity.offset, "\n")

    except Exception as err:
        print("Encountered exception. {}".format(err))
entity_recognition_example(client)

OutputOutput

Named Entities:

        Text:    trip   Category:        Event  SubCategory:     None
        Confidence Score:        0.61   Length:          4      Offset:          18

        Text:    Seattle        Category:        Location       SubCategory:     GPE
        Confidence Score:        0.82   Length:          7      Offset:          26

        Text:    last week      Category:        DateTime       SubCategory:     DateRange
        Confidence Score:        0.8    Length:          9      Offset:          34

EntitätsverknüpfungEntity Linking

Erstellen Sie eine neue Funktion namens entity_linking_example(), die den Client als Argument akzeptiert und anschließend die Funktion recognize_linked_entities() aufruft und die Ergebnisse durchläuft.Create a new function called entity_linking_example() that takes the client as an argument, then calls the recognize_linked_entities() function and iterates through the results. Das zurückgegebene Antwortobjekt enthält die Liste erkannter Entitäten in entities (sofern erfolgreich). Andernfalls enthält es eine Fehlermeldung (error).The returned response object will contain the list of detected entities in entities if successful, and an error if not. Da verknüpfte Entitäten eindeutig identifiziert werden, werden Vorkommen der gleichen Entität unter einem Objekt vom Typ entity als Liste von Objekten des Typs match gruppiert.Since linked entities are uniquely identified, occurrences of the same entity are grouped under a entity object as a list of match objects.

def entity_linking_example(client):

    try:
        documents = ["""Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975, 
        to develop and sell BASIC interpreters for the Altair 8800. 
        During his career at Microsoft, Gates held the positions of chairman,
        chief executive officer, president and chief software architect, 
        while also being the largest individual shareholder until May 2014."""]
        result = client.recognize_linked_entities(documents = documents)[0]

        print("Linked Entities:\n")
        for entity in result.entities:
            print("\tName: ", entity.name, "\tId: ", entity.data_source_entity_id, "\tUrl: ", entity.url,
            "\n\tData Source: ", entity.data_source)
            print("\tMatches:")
            for match in entity.matches:
                print("\t\tText:", match.text)
                print("\t\tConfidence Score: {0:.2f}".format(match.confidence_score))
                print("\t\tOffset: {}".format(match.offset))
                print("\t\tLength: {}".format(match.length))
            
    except Exception as err:
        print("Encountered exception. {}".format(err))
entity_linking_example(client)

OutputOutput

Linked Entities:

        Name:  Microsoft        Id:  Microsoft  Url:  https://en.wikipedia.org/wiki/Microsoft
        Data Source:  Wikipedia
        Matches:
                Text: Microsoft
                Confidence Score: 0.55
                Offset: 0
                Length: 9
                Text: Microsoft
                Confidence Score: 0.55
                Offset: 168
                Length: 9
        Name:  Bill Gates       Id:  Bill Gates         Url:  https://en.wikipedia.org/wiki/Bill_Gates
        Data Source:  Wikipedia
        Matches:
                Text: Bill Gates
                Confidence Score: 0.63
                Offset: 25
                Length: 10
                Text: Gates
                Confidence Score: 0.63
                Offset: 179
                Length: 5
        Name:  Paul Allen       Id:  Paul Allen         Url:  https://en.wikipedia.org/wiki/Paul_Allen
        Data Source:  Wikipedia
        Matches:
                Text: Paul Allen
                Confidence Score: 0.60
                Offset: 40
                Length: 10
        Name:  April 4  Id:  April 4    Url:  https://en.wikipedia.org/wiki/April_4
        Data Source:  Wikipedia
        Matches:
                Text: April 4
                Confidence Score: 0.32
                Offset: 54
                Length: 7
        Name:  BASIC    Id:  BASIC      Url:  https://en.wikipedia.org/wiki/BASIC
        Data Source:  Wikipedia
        Matches:
                Text: BASIC
                Confidence Score: 0.33
                Offset: 98
                Length: 5
        Name:  Altair 8800      Id:  Altair 8800        Url:  https://en.wikipedia.org/wiki/Altair_8800
        Data Source:  Wikipedia
        Matches:
                Text: Altair 8800
                Confidence Score: 0.88
                Offset: 125
                Length: 11

Erkennung von personenbezogenen InformationenPersonally Identifiable Information recognition

Erstellen Sie eine neue Funktion namens pii_recognition_example, die den Client als Argument akzeptiert und anschließend die Funktion recognize_pii_entities() aufruft und die Ergebnisse durchläuft.Create a new function called pii_recognition_example that takes the client as an argument, then calls the recognize_pii_entities() function and iterates through the results. Das zurückgegebene Antwortobjekt enthält die Liste erkannter Entitäten in entity (sofern erfolgreich). Andernfalls enthält es eine Fehlermeldung (error).The returned response object will contain the list of detected entities in entity if successful, and an error if not. Geben Sie für die erkannten Entitäten jeweils die Kategorie und Unterkategorie aus (sofern vorhanden).For each detected entity, print its Category and Sub-Category if exists.

def pii_recognition_example(client):
    documents = [
        "The employee's SSN is 859-98-0987.",
        "The employee's phone number is 555-555-5555."
    ]
    response = client.recognize_pii_entities(documents, language="en")
    result = [doc for doc in response if not doc.is_error]
    for doc in result:
        print("Redacted Text: {}".format(doc.redacted_text))
        for entity in doc.entities:
            print("Entity: {}".format(entity.text))
            print("\tCategory: {}".format(entity.category))
            print("\tConfidence Score: {}".format(entity.confidence_score))
            print("\tOffset: {}".format(entity.offset))
            print("\tLength: {}".format(entity.length))
pii_recognition_example(client)

OutputOutput

Redacted Text: The employee's SSN is ***********.
Entity: 859-98-0987
        Category: U.S. Social Security Number (SSN)
        Confidence Score: 0.65
        Offset: 22
        Length: 11
Redacted Text: The employee's phone number is ************.
Entity: 555-555-5555
        Category: Phone Number
        Confidence Score: 0.8
        Offset: 31
        Length: 12

Erstellen Sie eine neue Funktion namens entity_recognition_example, die den Client als Argument akzeptiert und anschließend die Funktion recognize_entities() aufruft und die Ergebnisse durchläuft.Create a new function called entity_recognition_example that takes the client as an argument, then calls the recognize_entities() function and iterates through the results. Das zurückgegebene Antwortobjekt enthält die Liste erkannter Entitäten in entity (sofern erfolgreich). Andernfalls enthält es eine Fehlermeldung (error).The returned response object will contain the list of detected entities in entity if successful, and an error if not. Geben Sie für die erkannten Entitäten jeweils die Kategorie und Unterkategorie aus (sofern vorhanden).For each detected entity, print its Category and Sub-Category if exists.

def entity_recognition_example(client):

    try:
        documents = ["I had a wonderful trip to Seattle last week."]
        result = client.recognize_entities(documents = documents)[0]

        print("Named Entities:\n")
        for entity in result.entities:
            print("\tText: \t", entity.text, "\tCategory: \t", entity.category, "\tSubCategory: \t", entity.subcategory,
                    "\n\tConfidence Score: \t", round(entity.confidence_score, 2), "\n")

    except Exception as err:
        print("Encountered exception. {}".format(err))
entity_recognition_example(client)

OutputOutput

Named Entities:

        Text:    trip   Category:        Event  SubCategory:     None
        Confidence Score:        0.61

        Text:    Seattle        Category:        Location       SubCategory:     GPE
        Confidence Score:        0.82

        Text:    last week      Category:        DateTime       SubCategory:     DateRange
        Confidence Score:        0.8

EntitätsverknüpfungEntity Linking

Erstellen Sie eine neue Funktion namens entity_linking_example(), die den Client als Argument akzeptiert und anschließend die Funktion recognize_linked_entities() aufruft und die Ergebnisse durchläuft.Create a new function called entity_linking_example() that takes the client as an argument, then calls the recognize_linked_entities() function and iterates through the results. Das zurückgegebene Antwortobjekt enthält die Liste erkannter Entitäten in entities (sofern erfolgreich). Andernfalls enthält es eine Fehlermeldung (error).The returned response object will contain the list of detected entities in entities if successful, and an error if not. Da verknüpfte Entitäten eindeutig identifiziert werden, werden Vorkommen der gleichen Entität unter einem Objekt vom Typ entity als Liste von Objekten des Typs match gruppiert.Since linked entities are uniquely identified, occurrences of the same entity are grouped under a entity object as a list of match objects.

def entity_linking_example(client):

    try:
        documents = ["""Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975, 
        to develop and sell BASIC interpreters for the Altair 8800. 
        During his career at Microsoft, Gates held the positions of chairman,
        chief executive officer, president and chief software architect, 
        while also being the largest individual shareholder until May 2014."""]
        result = client.recognize_linked_entities(documents = documents)[0]

        print("Linked Entities:\n")
        for entity in result.entities:
            print("\tName: ", entity.name, "\tId: ", entity.data_source_entity_id, "\tUrl: ", entity.url,
            "\n\tData Source: ", entity.data_source)
            print("\tMatches:")
            for match in entity.matches:
                print("\t\tText:", match.text)
                print("\t\tConfidence Score: {0:.2f}".format(match.confidence_score))
            
    except Exception as err:
        print("Encountered exception. {}".format(err))
entity_linking_example(client)

OutputOutput

Linked Entities:

        Name:  Altair 8800      Id:  Altair 8800        Url:  https://en.wikipedia.org/wiki/Altair_8800
        Data Source:  Wikipedia
        Matches:
                Text: Altair 8800
                Confidence Score: 0.88
        Name:  Bill Gates       Id:  Bill Gates         Url:  https://en.wikipedia.org/wiki/Bill_Gates
        Data Source:  Wikipedia
        Matches:
                Text: Bill Gates
                Confidence Score: 0.63
                Text: Gates
                Confidence Score: 0.63
        Name:  Paul Allen       Id:  Paul Allen         Url:  https://en.wikipedia.org/wiki/Paul_Allen
        Data Source:  Wikipedia
        Matches:
                Text: Paul Allen
                Confidence Score: 0.60
        Name:  Microsoft        Id:  Microsoft  Url:  https://en.wikipedia.org/wiki/Microsoft
        Data Source:  Wikipedia
        Matches:
                Text: Microsoft
                Confidence Score: 0.55
                Text: Microsoft
                Confidence Score: 0.55
        Name:  April 4  Id:  April 4    Url:  https://en.wikipedia.org/wiki/April_4
        Data Source:  Wikipedia
        Matches:
                Text: April 4
                Confidence Score: 0.32
        Name:  BASIC    Id:  BASIC      Url:  https://en.wikipedia.org/wiki/BASIC
        Data Source:  Wikipedia
        Matches:
                Text: BASIC
                Confidence Score: 0.33

Rufen Sie mit dem zuvor erstellten Client die Funktion entities() auf, um das Ergebnis abzurufen.Using the client created earlier, call the entities() function and get the result. Durchlaufen Sie dann die Ergebnisse, und geben Sie für jedes Dokument die ID und die enthaltenen Entitäten aus.Then iterate through the results, and print each document's ID, and the entities contained in it.

def entity_recognition():
    
    client = authenticateClient()

    try:
        documents = [
            {"id": "1", "language": "en", "text": "Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975, to develop and sell BASIC interpreters for the Altair 8800."},
            {"id": "2", "language": "es",
                "text": "La sede principal de Microsoft se encuentra en la ciudad de Redmond, a 21 kilómetros de Seattle."}
        ]
        response = client.recognize_entities(documents=documents)

        for document in response:
            print("Document Id: ", document.id)
            print("\tKey Entities:")
            for entity in document.entities:
                print("\t\t", "NAME: ", entity.text, "\tCategory: ", entity.category,
                      "\tSub-Category: ", entity.subcategory, "\tConfidence Score: ", entity.confidence_score)

    except Exception as err:
        print("Encountered exception. {}".format(err))
entity_recognition()

OutputOutput

Document ID: 1
        Name: Microsoft,        Type: Organization,     Sub-Type: N/A
        Offset: 0, Length: 9,   Score: 1.0

        Name: Bill Gates,       Type: Person,   Sub-Type: N/A
        Offset: 25, Length: 10, Score: 0.999847412109375

        Name: Paul Allen,       Type: Person,   Sub-Type: N/A
        Offset: 40, Length: 10, Score: 0.9988409876823425

        Name: April 4,  Type: Other,    Sub-Type: N/A
        Offset: 54, Length: 7,  Score: 0.8

        Name: April 4, 1975,    Type: DateTime, Sub-Type: Date
        Offset: 54, Length: 13, Score: 0.8

        Name: BASIC,    Type: Other,    Sub-Type: N/A
        Offset: 89, Length: 5,  Score: 0.8

        Name: Altair 8800,      Type: Other,    Sub-Type: N/A
        Offset: 116, Length: 11,        Score: 0.8

Document ID: 2
        Name: Microsoft,        Type: Organization,     Sub-Type: N/A
        Offset: 21, Length: 9,  Score: 0.999755859375

        Name: Redmond (Washington),     Type: Location, Sub-Type: N/A
        Offset: 60, Length: 7,  Score: 0.9911284446716309

        Name: 21 kilómetros,    Type: Quantity, Sub-Type: Dimension
        Offset: 71, Length: 13, Score: 0.8

        Name: Seattle,  Type: Location, Sub-Type: N/A
        Offset: 88, Length: 7,  Score: 0.9998779296875

Erstellen Sie eine neue Funktion namens key_phrase_extraction_example(), die den Client als Argument akzeptiert und anschließend die Funktion extract_key_phrases() aufruft.Create a new function called key_phrase_extraction_example() that takes the client as an argument, then calls the extract_key_phrases() function. Das Ergebnis enthält bei erfolgreicher Ausführung die Liste der erkannten Schlüsselbegriffe in key_phrases und andernfalls error.The result will contain the list of detected key phrases in key_phrases if successful, and an error if not. Geben Sie alle erkannten Schlüsselbegriffe aus.Print any detected key phrases.

def key_phrase_extraction_example(client):

    try:
        documents = ["My cat might need to see a veterinarian."]

        response = client.extract_key_phrases(documents = documents)[0]

        if not response.is_error:
            print("\tKey Phrases:")
            for phrase in response.key_phrases:
                print("\t\t", phrase)
        else:
            print(response.id, response.error)

    except Exception as err:
        print("Encountered exception. {}".format(err))
        
key_phrase_extraction_example(client)

OutputOutput

    Key Phrases:
         cat
         veterinarian

Erstellen Sie eine neue Funktion namens key_phrase_extraction_example(), die den Client als Argument akzeptiert und anschließend die Funktion extract_key_phrases() aufruft.Create a new function called key_phrase_extraction_example() that takes the client as an argument, then calls the extract_key_phrases() function. Das Ergebnis enthält bei erfolgreicher Ausführung die Liste der erkannten Schlüsselbegriffe in key_phrases und andernfalls error.The result will contain the list of detected key phrases in key_phrases if successful, and an error if not. Geben Sie alle erkannten Schlüsselbegriffe aus.Print any detected key phrases.

def key_phrase_extraction_example(client):

    try:
        documents = ["My cat might need to see a veterinarian."]

        response = client.extract_key_phrases(documents = documents)[0]

        if not response.is_error:
            print("\tKey Phrases:")
            for phrase in response.key_phrases:
                print("\t\t", phrase)
        else:
            print(response.id, response.error)

    except Exception as err:
        print("Encountered exception. {}".format(err))
        
key_phrase_extraction_example(client)

OutputOutput

    Key Phrases:
         cat
         veterinarian

Rufen Sie mit dem zuvor erstellten Client die Funktion key_phrases() auf, um das Ergebnis abzurufen.Using the client created earlier, call the key_phrases() function and get the result. Durchlaufen Sie dann die Ergebnisse, und geben Sie für jedes Dokument die ID und die enthaltenen Schlüsselausdrücke aus.Then iterate through the results, and print each document's ID, and the key phrases contained in it.

def key_phrases():
    
    client = authenticateClient()

    try:
        documents = [
            {"id": "1", "language": "ja", "text": "猫は幸せ"},
            {"id": "2", "language": "de",
                "text": "Fahrt nach Stuttgart und dann zum Hotel zu Fu."},
            {"id": "3", "language": "en",
                "text": "My cat might need to see a veterinarian."},
            {"id": "4", "language": "es", "text": "A mi me encanta el fútbol!"}
        ]

        for document in documents:
            print(
                "Asking key-phrases on '{}' (id: {})".format(document['text'], document['id']))

        response = client.extract_key_phrases(documents=documents)

        for document in response:
            print("Document Id: ", document.id)
            print("\tKey Phrases:")
            for phrase in document.key_phrases:
                print("\t\t", phrase)

    except Exception as err:
        print("Encountered exception. {}".format(err))
key_phrases()

OutputOutput

Document ID: 1
         Key phrases:
                幸せ
Document ID: 2
         Key phrases:
                Stuttgart
                Hotel
                Fahrt
                Fu
Document ID: 3
         Key phrases:
                cat
                veterinarian
Document ID: 4
         Key phrases:
                fútbol

Erstellen Sie eine neue Funktion namens analyze_example(), die den Client als Argument akzeptiert und anschließend die Funktion begin_analyze() aufruft.Create a new function called analyze_example() that takes the client as an argument, then calls the begin_analyze() function. Daraus resultiert ein zeitintensiver Vorgang, aus dem Ergebnisse abgerufen werden.The result will be a long running operation which will be polled for results.

    def analyze_example(client):
        documents = [
            "Microsoft was founded by Bill Gates and Paul Allen."
        ]

        poller = text_analytics_client.begin_analyze(
            documents,
            display_name="Sample Text Analysis",
            entities_recognition_tasks=[EntitiesRecognitionTask()]
        )

        result = poller.result()

        for page in result:
            for task in page.entities_recognition_results:
                print("Results of Entities Recognition task:")
                
                docs = [doc for doc in task.results if not doc.is_error]
                for idx, doc in enumerate(docs):
                    print("\nDocument text: {}".format(documents[idx]))
                    for entity in doc.entities:
                        print("Entity: {}".format(entity.text))
                        print("...Category: {}".format(entity.category))
                        print("...Confidence Score: {}".format(entity.confidence_score))
                        print("...Offset: {}".format(entity.offset))
                    print("------------------------------------------")

analyze_example(client)

AusgabeOutput

Results of Entities Recognition task:
Document text: Microsoft was founded by Bill Gates and Paul Allen.
Entity: Microsoft
...Category: Organization
...Confidence Score: 0.83
...Offset: 0
Entity: Bill Gates
...Category: Person
...Confidence Score: 0.85
...Offset: 25
Entity: Paul Allen
...Category: Person
...Confidence Score: 0.9
...Offset: 40
------------------------------------------

Sie können den Analyze-Vorgang auch verwenden, um personenbezogene Informationen zu erkennen und Schlüsselbegriffe zu extrahieren.You can also use the Analyze operation to detect PII and key phrase extraction. Das Analyze-Beispiel finden Sie auf GitHub.See the Analyze sample on GitHub.

Dieses Feature ist in Version 3.0 nicht verfügbar.This feature is not available in version 3.0.

Dieses Feature ist in Version 2.1 nicht verfügbar.This feature is not available in version 2.1.

Referenzdokumentation zu Version 3.1v3.1 Reference documentation

Referenzdokumentation zu Version 3v3 Reference documentation

curl -X POST https://<your-text-analytics-endpoint-here>/text/analytics/v3.1-preview.3/sentiment?opinionMining=true \
-H "Content-Type: application/json" \
-H "Ocp-Apim-Subscription-Key: <your-text-analytics-key-here>" \
-d '{ documents: [{ id: "1", text: "The customer service here is really good."}]}'

JSON-AntwortJSON response

{
   "documents":[
      {
         "id":"1",
         "sentiment":"positive",
         "confidenceScores":{
            "positive":1.0,
            "neutral":0.0,
            "negative":0.0
         },
         "sentences":[
            {
               "sentiment":"positive",
               "confidenceScores":{
                  "positive":1.0,
                  "neutral":0.0,
                  "negative":0.0
               },
               "offset":0,
               "length":41,
               "text":"The customer service here is really good.",
               "aspects":[
                  {
                     "sentiment":"positive",
                     "confidenceScores":{
                        "positive":1.0,
                        "negative":0.0
                     },
                     "offset":4,
                     "length":16,
                     "text":"customer service",
                     "relations":[
                        {
                           "relationType":"opinion",
                           "ref":"#/documents/0/sentences/0/opinions/0"
                        }
                     ]
                  }
               ],
               "opinions":[
                  {
                     "sentiment":"positive",
                     "confidenceScores":{
                        "positive":1.0,
                        "negative":0.0
                     },
                     "offset":36,
                     "length":4,
                     "text":"good",
                     "isNegated":false
                  }
               ]
            }
         ],
         "warnings":[
            
         ]
      }
   ],
   "errors":[
      
   ],
   "modelVersion":"2020-04-01"
}

curl -X POST https://<your-text-analytics-endpoint-here>/text/analytics/v3.0/sentiment/ \
-H "Content-Type: application/json" \
-H "Ocp-Apim-Subscription-Key: <your-text-analytics-key-here>" \
-d '{ documents: [{ id: "1", text: "I had the best day of my life. I wish you were there with me."}]}'

JSON-AntwortJSON response

{
  "documents":[
    {
      "id":"1",
      "sentiment":"positive",
      "documentScores":{
        "positive":1.0,
        "neutral":0.0,
        "negative":0.0
      },
      "sentences":[
        {
          "sentiment":"positive",
          "sentenceScores":{
            "positive":1.0,
            "neutral":0.0,
            "negative":0.0
          },
          "offset":0,
          "length":30
        }
      ]
    }
  ],
  "errors":[
  ],
  "modelVersion":"2019-10-01"
}

curl -X POST https://<your-text-analytics-endpoint-here>/text/analytics/v3.1-preview.3/languages/ \
-H "Content-Type: application/json" \
-H "Ocp-Apim-Subscription-Key: <your-text-analytics-key-here>" \
-d '{ documents: [{ id: "1", text: "This is a document written in English."}]}'

JSON-AntwortJSON response

{
   "documents":[
      {
         "id":"1",
         "detectedLanguage":{
            "name":"English",
            "iso6391Name":"en",
            "confidenceScore":0.99
         },
         "warnings":[
            
         ]
      }
   ],
   "errors":[
      
   ],
   "modelVersion":"2020-09-01"
}

curl -X POST https://<your-text-analytics-endpoint-here>/text/analytics/v3.0/languages/ \
-H "Content-Type: application/json" \
-H "Ocp-Apim-Subscription-Key: <your-text-analytics-key-here>" \
-d '{ documents: [{ id: "1", text: "This is a document written in English."}]}'

JSON-AntwortJSON response

{
   "documents":[
      {
         "id":"1",
         "detectedLanguage":{
            "name":"English",
            "iso6391Name":"en",
            "confidenceScore":0.99
         },
         "warnings":[
            
         ]
      }
   ],
   "errors":[
      
   ],
   "modelVersion":"2020-09-01"
}

curl -X POST https://<your-text-analytics-endpoint-here>/text/analytics/v3.1-preview.3/entities/recognition/general \
-H "Content-Type: application/json" \
-H "Ocp-Apim-Subscription-Key: <your-text-analytics-key-here>" \
-d '{ documents: [{ id: "1", language:"en", text: "I had a wonderful trip to Seattle last week."}]}'

JSON-AntwortJSON response

{
   "documents":[
      {
         "id":"1",
         "entities":[
            {
               "text":"trip",
               "category":"Event",
               "offset":18,
               "length":4,
               "confidenceScore":0.61
            },
            {
               "text":"Seattle",
               "category":"Location",
               "subcategory":"GPE",
               "offset":26,
               "length":7,
               "confidenceScore":0.82
            },
            {
               "text":"last week",
               "category":"DateTime",
               "subcategory":"DateRange",
               "offset":34,
               "length":9,
               "confidenceScore":0.8
            }
         ],
         "warnings":[
            
         ]
      }
   ],
   "errors":[
      
   ],
   "modelVersion":"2020-04-01"
}

Erkennen personenbezogener InformationenDetecting personally identifying information

1. Kopieren Sie den Befehl in einen Text-Editor.Copy the command into a text editor.
2. Nehmen Sie die folgenden Änderungen im Befehl vor, falls dies erforderlich ist:Make the following changes in the command where needed:
   1. Ersetzen Sie den Wert <your-text-analytics-key-here> durch Ihren Schlüssel.Replace the value <your-text-analytics-key-here> with your key.
   2. Ersetzen Sie den ersten Teil der Anforderungs-URL (<your-text-analytics-endpoint-here>) durch Ihre Endpunkt-URL.Replace the first part of the request URL <your-text-analytics-endpoint-here> with the your own endpoint URL.
3. Öffnen Sie ein Eingabeaufforderungsfenster.Open a command prompt window.
4. Fügen Sie den Befehl aus dem Text-Editor in das Eingabeaufforderungsfenster ein, und führen Sie den Befehl aus.Paste the command from the text editor into the command prompt window, and then run the command.

curl -X POST https://your-text-analytics-endpoint-here>/text/analytics/v3.1-preview.3/entities/recognition/pii \
-H "Content-Type: application/json" \
-H "Ocp-Apim-Subscription-Key: <your-text-analytics-key-here>" \
-d '{ documents: [{ id: "1", language:"en", text: "Call our office at 312-555-1234, or send an email to support@contoso.com"}]}'

JSON-AntwortJSON response

{
   "documents":[
      {
         "redactedText":"Insurance policy for *** on file 123-12-1234 is here by approved.",
         "id":"1",
         "entities":[
            {
               "text":"SSN",
               "category":"Organization",
               "offset":21,
               "length":3,
               "confidenceScore":0.45
            }
         ],
         "warnings":[
            
         ]
      }
   ],
   "errors":[
      
   ],
   "modelVersion":"2020-07-01"
}

curl -X POST https://<your-text-analytics-endpoint-here>/text/analytics/v3.0/entities/recognition/general \
-H "Content-Type: application/json" \
-H "Ocp-Apim-Subscription-Key: <your-text-analytics-key-here>" \
-d '{ documents: [{ id: "1", language:"en", text: "I had a wonderful trip to Seattle last week."}]}'

JSON-AntwortJSON response

{
   "documents":[
      {
         "id":"1",
         "entities":[
            {
               "text":"trip",
               "category":"Event",
               "offset":18,
               "length":4,
               "confidenceScore":0.61
            },
            {
               "text":"Seattle",
               "category":"Location",
               "subcategory":"GPE",
               "offset":26,
               "length":7,
               "confidenceScore":0.82
            },
            {
               "text":"last week",
               "category":"DateTime",
               "subcategory":"DateRange",
               "offset":34,
               "length":9,
               "confidenceScore":0.8
            }
         ],
         "warnings":[
            
         ]
      }
   ],
   "errors":[
      
   ],
   "modelVersion":"2020-04-01"
}

curl -X POST https://<your-text-analytics-endpoint-here>/text/analytics/v3.1-preview.3/entities/linking \
-H "Content-Type: application/json" \
-H "Ocp-Apim-Subscription-Key: <your-text-analytics-key-here>" \
-d '{ documents: [{ id: "1", language:"en", text: "Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975."}]}'

JSON-AntwortJSON response

{
   "documents":[
      {
         "id":"1",
         "entities":[
            {
               "bingId":"a093e9b9-90f5-a3d5-c4b8-5855e1b01f85",
               "name":"Microsoft",
               "matches":[
                  {
                     "text":"Microsoft",
                     "offset":0,
                     "length":9,
                     "confidenceScore":0.48
                  }
               ],
               "language":"en",
               "id":"Microsoft",
               "url":"https://en.wikipedia.org/wiki/Microsoft",
               "dataSource":"Wikipedia"
            },
            {
               "bingId":"0d47c987-0042-5576-15e8-97af601614fa",
               "name":"Bill Gates",
               "matches":[
                  {
                     "text":"Bill Gates",
                     "offset":25,
                     "length":10,
                     "confidenceScore":0.52
                  }
               ],
               "language":"en",
               "id":"Bill Gates",
               "url":"https://en.wikipedia.org/wiki/Bill_Gates",
               "dataSource":"Wikipedia"
            },
            {
               "bingId":"df2c4376-9923-6a54-893f-2ee5a5badbc7",
               "name":"Paul Allen",
               "matches":[
                  {
                     "text":"Paul Allen",
                     "offset":40,
                     "length":10,
                     "confidenceScore":0.54
                  }
               ],
               "language":"en",
               "id":"Paul Allen",
               "url":"https://en.wikipedia.org/wiki/Paul_Allen",
               "dataSource":"Wikipedia"
            },
            {
               "bingId":"52535f87-235e-b513-54fe-c03e4233ac6e",
               "name":"April 4",
               "matches":[
                  {
                     "text":"April 4",
                     "offset":54,
                     "length":7,
                     "confidenceScore":0.38
                  }
               ],
               "language":"en",
               "id":"April 4",
               "url":"https://en.wikipedia.org/wiki/April_4",
               "dataSource":"Wikipedia"
            }
         ],
         "warnings":[
            
         ]
      }
   ],
   "errors":[
      
   ],
   "modelVersion":"2020-02-01"
}

curl -X POST https://<your-text-analytics-endpoint-here>/text/analytics/v3.0/entities/linking \
-H "Content-Type: application/json" \
-H "Ocp-Apim-Subscription-Key: <your-text-analytics-key-here>" \
-d '{ documents: [{ id: "1", language:"en", text: "Microsoft was founded by Bill Gates and Paul Allen on April 4, 1975."}]}'

JSON-AntwortJSON response

{
   "documents":[
      {
         "id":"1",
         "entities":[
            {
               "name":"Microsoft",
               "matches":[
                  {
                     "text":"Microsoft",
                     "offset":0,
                     "length":9,
                     "confidenceScore":0.48
                  }
               ],
               "language":"en",
               "id":"Microsoft",
               "url":"https://en.wikipedia.org/wiki/Microsoft",
               "dataSource":"Wikipedia"
            },
            {
               "name":"Bill Gates",
               "matches":[
                  {
                     "text":"Bill Gates",
                     "offset":25,
                     "length":10,
                     "confidenceScore":0.52
                  }
               ],
               "language":"en",
               "id":"Bill Gates",
               "url":"https://en.wikipedia.org/wiki/Bill_Gates",
               "dataSource":"Wikipedia"
            },
            {
               "name":"Paul Allen",
               "matches":[
                  {
                     "text":"Paul Allen",
                     "offset":40,
                     "length":10,
                     "confidenceScore":0.54
                  }
               ],
               "language":"en",
               "id":"Paul Allen",
               "url":"https://en.wikipedia.org/wiki/Paul_Allen",
               "dataSource":"Wikipedia"
            },
            {
               "name":"April 4",
               "matches":[
                  {
                     "text":"April 4",
                     "offset":54,
                     "length":7,
                     "confidenceScore":0.38
                  }
               ],
               "language":"en",
               "id":"April 4",
               "url":"https://en.wikipedia.org/wiki/April_4",
               "dataSource":"Wikipedia"
            }
         ],
         "warnings":[
            
         ]
      }
   ],
   "errors":[
      
   ],
   "modelVersion":"2020-02-01"
}

curl -X POST https://<your-text-analytics-endpoint-here>/text/analytics/v3.1-preview.3/keyPhrases \
-H "Content-Type: application/json" \
-H "Ocp-Apim-Subscription-Key: <your-text-analytics-key-here>" \
-d '{ documents: [{ id: "1", language:"en", text: "Hello world. This is some input text that I love."}]}'

{
   "documents":[
      {
         "id":"1",
         "keyPhrases":[
            "wonderful trip",
            "Seattle",
            "week"
         ],
         "warnings":[
            
         ]
      }
   ],
   "errors":[
      
   ],
   "modelVersion":"2020-07-01"
}

curl -X POST https://<your-text-analytics-endpoint-here>/text/analytics/v3.0/keyPhrases \
-H "Content-Type: application/json" \
-H "Ocp-Apim-Subscription-Key: <your-text-analytics-key-here>" \
-d '{ documents: [{ id: "1", language:"en", text: "I had a wonderful trip to Seattle last week."}]}'

JSON-AntwortJSON response

{
   "documents":[
      {
         "id":"1",
         "keyPhrases":[
            "wonderful trip",
            "Seattle",
            "week"
         ],
         "warnings":[
            
         ]
      }
   ],
   "errors":[
      
   ],
   "modelVersion":"2020-07-01"
}