快速入门：使用文本分析客户端库和 REST APIQuickstart: Use the Text Analytics client library and REST API

v3.1 参考文档 | v3.1 库源代码 | v3.1 包 (NuGet) | v3.1 示例v3.1 Reference documentation | v3.1 Library source code | v3.1 Package (NuGet) | v3.1 Samples

v3 参考文档 | v3 库源代码 | v3 包(NuGet) | v3 示例v3 Reference documentation | v3 Library source code | v3 Package (NuGet) | v3 Samples

右键单击 解决方案资源管理器 中的解决方案，然后选择“管理 NuGet 包”，以便安装客户端库。Install the client library by right-clicking on the solution in the Solution Explorer and selecting Manage NuGet Packages. 在打开的包管理器中选择“浏览”，搜索 Azure.AI.TextAnalytics。In the package manager that opens select Browse and search for Azure.AI.TextAnalytics. 选中“包括预发行版”框，选择版本 5.1.0-beta.5，然后选择“安装”。Check the include prerelase box, select version 5.1.0-beta.5, and then Install. 也可使用包管理器控制台。You can also use the Package Manager Console.

右键单击 解决方案资源管理器 中的解决方案，然后选择“管理 NuGet 包”，以便安装客户端库。Install the client library by right-clicking on the solution in the Solution Explorer and selecting Manage NuGet Packages. 在打开的包管理器中选择“浏览”，搜索 Azure.AI.TextAnalytics。In the package manager that opens select Browse and search for Azure.AI.TextAnalytics. 选择版本 5.0.0，然后选择“安装”。Select version 5.0.0, and then Install. 也可使用包管理器控制台。You can also use the Package Manager Console.

打开 program.cs 文件并添加以下 using 指令：Open the program.cs file and add the following using directives:

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

在应用程序的 Program 类中，为资源的密钥和终结点创建变量。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>");

替换应用程序的 Main 方法。Replace the application's Main method. 稍后将定义此处调用的方法。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();
}

打开 program.cs 文件并添加以下 using 指令：Open the program.cs file and add the following using directives:

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

在应用程序的 Program 类中，为资源的密钥和终结点创建变量。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>");

替换应用程序的 Main 方法。Replace the application's Main method. 稍后将定义此处调用的方法。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();
}

请确保先前的 main 方法使用终结点和凭据创建新的客户端对象。Make sure your main method from earlier creates a new client object with your endpoint and credentials.

var client = new TextAnalyticsClient(endpoint, credentials);

请确保先前的 main 方法使用终结点和凭据创建新的客户端对象。Make sure your main method from earlier creates a new client object with your endpoint and credentials.

var client = new TextAnalyticsClient(endpoint, credentials);

创建一个名为 SentimentAnalysisExample() 的新函数，该函数接受你之前创建的客户端，并调用其 AnalyzeSentiment() 函数。Create a new function called SentimentAnalysisExample() that takes the client that you created earlier, and call its AnalyzeSentiment() function. 如果成功，则返回的 Response<DocumentSentiment> 对象将包含整个输入文档的情绪标签和分数，以及每个句子的情绪分析。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. 如果发生错误，则会引发 RequestFailedException。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");
    }
}

输出Output

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

观点挖掘Opinion mining

创建一个名为 SentimentAnalysisWithOpinionMiningExample() 的新函数，该函数接受你之前创建的客户端，并使用 AnalyzeSentimentOptions 包中的 IncludeOpinionMining 选项调用其 AnalyzeSentimentBatch() 函数。Create a new function called SentimentAnalysisWithOpinionMiningExample() that takes the client that you created earlier, and call its AnalyzeSentimentBatch() function with IncludeOpinionMining option in the AnalyzeSentimentOptions bag. 返回的 AnalyzeSentimentResultCollection 对象将包含表示 Response<DocumentSentiment> 的 AnalyzeSentimentResult 的集合。The returned AnalyzeSentimentResultCollection object will contain the collection of AnalyzeSentimentResult in which represents Response<DocumentSentiment>. SentimentAnalysis() 和 SentimentAnalysisWithOpinionMiningExample() 的区别在于后者的每个句子都包含 SentenceOpinion（表明了所分析的角度和相关评价）。The difference between SentimentAnalysis() and SentimentAnalysisWithOpinionMiningExample() is that the latter will contain SentenceOpinion in each sentence, which shows an analyzed target and the related assessment(s). 如果发生错误，则会引发 RequestFailedException。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 (SentenceOpinion sentenceOpinion in sentence.Opinions)
            {
                Console.WriteLine($"\tTarget: {sentenceOpinion.Target.Text}, Value: {sentenceOpinion.Target.Sentiment}");
                Console.WriteLine($"\tTarget positive score: {sentenceOpinion.Target.ConfidenceScores.Positive:0.00}");
                Console.WriteLine($"\tTarget negative score: {sentenceOpinion.Target.ConfidenceScores.Negative:0.00}");
                foreach (AssessmentSentiment assessment in sentenceOpinion.Assessments)
                {
                    Console.WriteLine($"\t\tRelated Assessment: {assessment.Text}, Value: {assessment.Sentiment}");
                    Console.WriteLine($"\t\tRelated Assessment positive score: {assessment.ConfidenceScores.Positive:0.00}");
                    Console.WriteLine($"\t\tRelated Assessment negative score: {assessment.ConfidenceScores.Negative:0.00}");
                }
            }
        }
        Console.WriteLine($"\n");
    }
}

输出Output

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

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

Press any key to exit.

创建一个名为 SentimentAnalysisExample() 的新函数，该函数接受你之前创建的客户端，并调用其 AnalyzeSentiment() 函数。Create a new function called SentimentAnalysisExample() that takes the client that you created earlier, and call its AnalyzeSentiment() function. 如果成功，则返回的 Response<DocumentSentiment> 对象将包含整个输入文档的情绪标签和分数，以及每个句子的情绪分析。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. 如果发生错误，则会引发 RequestFailedException。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");
    }
}

输出Output

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

创建一个名为 LanguageDetectionExample() 的新函数，该函数接受你之前创建的客户端并调用其 DetectLanguage() 函数。Create a new function called LanguageDetectionExample() that takes the client that you created earlier, and call its DetectLanguage() function. 返回的 Response<DetectedLanguage> 对象会包含检测到的语言及其名称和 ISO-6391 代码。The returned Response<DetectedLanguage> object will contain the detected language along with its name and ISO-6391 code. 如果发生错误，则会引发 RequestFailedException。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");
}

输出Output

Language:
        French, ISO-6391: fr

创建一个名为 LanguageDetectionExample() 的新函数，该函数接受你之前创建的客户端并调用其 DetectLanguage() 函数。Create a new function called LanguageDetectionExample() that takes the client that you created earlier, and call its DetectLanguage() function. 返回的 Response<DetectedLanguage> 对象会包含检测到的语言及其名称和 ISO-6391 代码。The returned Response<DetectedLanguage> object will contain the detected language along with its name and ISO-6391 code. 如果发生错误，则会引发 RequestFailedException。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");
}

输出Output

Language:
        French, ISO-6391: fr

创建一个名为 EntityRecognitionExample() 的新函数，该函数接受你之前创建的客户端，调用其 RecognizeEntities() 函数并循环访问结果。Create a new function called EntityRecognitionExample() that takes the client that you created earlier, call its RecognizeEntities() function and iterate through the results. 返回的 Response<CategorizedEntityCollection> 对象将包含检测到的实体 CategorizedEntity 的集合。The returned Response<CategorizedEntityCollection> object will contain the collection of detected entities CategorizedEntity. 如果发生错误，则会引发 RequestFailedException。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");
    }
}

输出Output

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

实体链接Entity linking

创建一个名为 EntityLinkingExample() 的新函数，该函数接受你之前创建的客户端，调用其 RecognizeLinkedEntities() 函数并循环访问结果。Create a new function called EntityLinkingExample() that takes the client that you created earlier, call its RecognizeLinkedEntities() function and iterate through the results. 返回的 Response<LinkedEntityCollection> 对象将包含检测到的实体 LinkedEntity 的集合。The returned Response<LinkedEntityCollection> object will contain the collection of detected entities LinkedEntity. 如果发生错误，则会引发 RequestFailedException。If there was an error, it will throw a RequestFailedException. 由于链接实体是唯一标识的，因此同一实体的实例将以分组形式出现在 LinkedEntity 对象下，显示为 LinkedEntityMatch 对象的列表。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");
        }
    }
}

输出Output

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

个人身份信息识别Personally Identifiable Information recognition

创建一个名为 RecognizePIIExample() 的新函数，该函数接受你之前创建的客户端，调用其 RecognizePiiEntities() 函数并循环访问结果。Create a new function called RecognizePIIExample() that takes the client that you created earlier, call its RecognizePiiEntities() function and iterate through the results. 返回的 PiiEntityCollection 表示检测到的 PII 实体的列表。The returned PiiEntityCollection represents the list of detected PII entities. 如果发生错误，则会引发 RequestFailedException。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.");
    }
}

输出Output

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

创建一个名为 EntityRecognitionExample() 的新函数，该函数接受你之前创建的客户端，调用其 RecognizeEntities() 函数并循环访问结果。Create a new function called EntityRecognitionExample() that takes the client that you created earlier, call its RecognizeEntities() function and iterate through the results. 返回的 Response<IReadOnlyCollection<CategorizedEntity>> 对象将包含检测到的实体的列表。The returned Response<IReadOnlyCollection<CategorizedEntity>> object will contain the list of detected entities. 如果发生错误，则会引发 RequestFailedException。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");
    }
}

输出Output

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

实体链接Entity linking

创建一个名为 EntityLinkingExample() 的新函数，该函数接受你之前创建的客户端，调用其 RecognizeLinkedEntities() 函数并循环访问结果。Create a new function called EntityLinkingExample() that takes the client that you created earlier, call its RecognizeLinkedEntities() function and iterate through the results. 返回的 Response<IReadOnlyCollection<LinkedEntity>> 表示检测到的实体的列表。The returned Response<IReadOnlyCollection<LinkedEntity>> represents the list of detected entities. 如果发生错误，则会引发 RequestFailedException。If there was an error, it will throw a RequestFailedException. 由于链接实体是唯一标识的，因此同一实体的实例将以分组形式出现在 LinkedEntity 对象下，显示为 LinkedEntityMatch 对象的列表。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");
        }
    }
}

输出Output

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

创建一个名为 KeyPhraseExtractionExample() 的新函数，该函数接受你之前创建的客户端，并调用其 ExtractKeyPhrases() 函数。Create a new function called KeyPhraseExtractionExample() that takes the client that you created earlier, and call its ExtractKeyPhrases() function. 返回的 <Response<KeyPhraseCollection> 对象将包含检测到的关键短语的列表。The returned <Response<KeyPhraseCollection> object will contain the list of detected key phrases. 如果发生错误，则会引发 RequestFailedException。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}");
    }
}

输出Output

Key phrases:
    cat
    veterinarian

创建一个名为 KeyPhraseExtractionExample() 的新函数，该函数接受你之前创建的客户端，并调用其 ExtractKeyPhrases() 函数。Create a new function called KeyPhraseExtractionExample() that takes the client that you created earlier, and call its ExtractKeyPhrases() function. 返回的 <Response<IReadOnlyCollection<string>> 对象将包含检测到的关键短语的列表。The returned <Response<IReadOnlyCollection<string>> object will contain the list of detected key phrases. 如果发生错误，则会引发 RequestFailedException。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}");
    }
}

输出Output

Key phrases:
    cat
    veterinarian

参考文档 | 库源代码 | 包 | 示例Reference documentation | Library source code | Package | Samples

参考文档 | 库源代码 | 包 | 示例Reference documentation | Library source code | Package | Samples

在首选 IDE 或开发环境中创建 Maven 项目。Create a Maven project in your preferred IDE or development environment. 然后在项目的 pom.xml 文件中，添加以下依赖项。Then add the following dependency to your project's pom.xml file. 可联机找到用于其他生成工具的实现语法。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.5</version>
    </dependency>
</dependencies>

在首选 IDE 或开发环境中创建 Maven 项目。Create a Maven project in your preferred IDE or development environment. 然后在项目的 pom.xml 文件中，添加以下依赖项。Then add the following dependency to your project's pom.xml file. 可联机找到用于其他生成工具的实现语法。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>

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)
}

创建一个名为 sentimentAnalysisExample() 的新函数，该函数接受你之前创建的客户端，并调用其 analyzeSentiment() 函数。Create a new function called sentimentAnalysisExample() that takes the client that you created earlier, and call its analyzeSentiment() function. 如果成功，则返回的 AnalyzeSentimentResult 对象将包含 documentSentiment 和 sentenceSentiments，否则将包含 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());
        }
    }
}

输出Output

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.

观点挖掘Opinion mining

若要使用观点挖掘执行情绪分析，请创建一个名为 sentimentAnalysisWithOpinionMiningExample() 的新函数（该函数接受之前创建的客户端），并通过设置选项对象 AnalyzeSentimentOptions 调用其 analyzeSentiment() 函数。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. 如果成功，则返回的 AnalyzeSentimentResult 对象将包含 documentSentiment 和 sentenceSentiments，否则将包含 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.getOpinions().forEach(opinion -> {
             TargetSentiment targetSentiment = opinion.getTarget();
             System.out.printf("\t\tTarget sentiment: %s, target text: %s%n", targetSentiment.getSentiment(),
                     targetSentiment.getText());
             for (AssessmentSentiment assessmentSentiment : opinion.getAssessments()) {
                 System.out.printf("\t\t\t'%s' assessment sentiment because of \"%s\". Is the assessment negated: %s.%n",
                         assessmentSentiment.getSentiment(), assessmentSentiment.getText(), assessmentSentiment.isNegated());
             }
         });
     });
 }

输出Output

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.
        Target sentiment: negative, target text: atmosphere
            'negative' assessment sentiment because of "bad". Is the assessment negated: false.
    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.
        Target sentiment: negative, target text: Staff
            'negative' assessment sentiment because of "friendly". Is the assessment negated: true.
            'negative' assessment sentiment because of "helpful". Is the assessment negated: true.

创建一个名为 sentimentAnalysisExample() 的新函数，该函数接受你之前创建的客户端，并调用其 analyzeSentiment() 函数。Create a new function called sentimentAnalysisExample() that takes the client that you created earlier, and call its analyzeSentiment() function. 如果成功，则返回的 AnalyzeSentimentResult 对象将包含 documentSentiment 和 sentenceSentiments，否则将包含 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());
        }
    }
}

输出Output

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.

创建一个名为 recognizeEntitiesExample() 的新函数，该函数接受你之前创建的客户端，并调用其 recognizeEntities() 函数。Create a new function called recognizeEntitiesExample() that takes the client that you created earlier, and call its recognizeEntities() function. 如果成功，则返回的 CategorizedEntityCollection 对象将包含 CategorizedEntity 的列表，否则将包含 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());
    }
}

输出Output

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.

实体链接Entity linking

创建一个名为 recognizeLinkedEntitiesExample() 的新函数，该函数接受你之前创建的客户端，并调用其 recognizeLinkedEntities() 函数。Create a new function called recognizeLinkedEntitiesExample() that takes the client that you created earlier, and call its recognizeLinkedEntities() function. 如果成功，则返回的 LinkedEntityCollection 对象将包含 LinkedEntity 的列表，否则将包含 errorMessage。The returned LinkedEntityCollection object will contain a list of LinkedEntity if successful, or an errorMessage if not. 由于链接实体是唯一标识的，因此同一实体的实例将以分组形式出现在 LinkedEntity 对象下，显示为 LinkedEntityMatch 对象的列表。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());
        }
    }
}

输出Output

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

个人身份信息识别Personally Identifiable Information Recognition

创建一个名为 recognizePiiEntitiesExample() 的新函数，该函数接受你之前创建的客户端，并调用其 recognizePiiEntities() 函数。Create a new function called recognizePiiEntitiesExample() that takes the client that you created earlier, and call its recognizePiiEntities() function. 如果成功，则返回的 PiiEntityCollection 对象将包含 PiiEntity 的列表，否则将包含 errorMessage。The returned PiiEntityCollection object will contain a list of PiiEntity if successful, or an errorMessage if not. 它还将包含已修订的文本，该文本由输入文本组成，其中所有可识别实体均替换为 *****。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()));
}

输出Output

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.

创建一个名为 recognizeEntitiesExample() 的新函数，该函数接受你之前创建的客户端，并调用其 recognizeEntities() 函数。Create a new function called recognizeEntitiesExample() that takes the client that you created earlier, and call its recognizeEntities() function. 如果成功，则返回的 CategorizedEntityCollection 对象将包含 CategorizedEntity 的列表，否则将包含 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());
    }
}

输出Output

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.

实体链接Entity linking

创建一个名为 recognizeLinkedEntitiesExample() 的新函数，该函数接受你之前创建的客户端，并调用其 recognizeLinkedEntities() 函数。Create a new function called recognizeLinkedEntitiesExample() that takes the client that you created earlier, and call its recognizeLinkedEntities() function. 如果成功，则返回的 LinkedEntityCollection 对象将包含 LinkedEntity 的列表，否则将包含 errorMessage。The returned LinkedEntityCollection object will contain a list of LinkedEntity if successful, or an errorMessage if not. 由于链接实体是唯一标识的，因此同一实体的实例将以分组形式出现在 LinkedEntity 对象下，显示为 LinkedEntityMatch 对象的列表。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());
        }
    }
}

输出Output

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

v3 参考文档 | v3 库源代码 | v3 包(NPM) | v3 示例v3 Reference documentation | v3 Library source code | v3 Package (NPM) | v3 Samples

v3 参考文档 | v3 库源代码 | v3 包(NPM) | v3 示例v3 Reference documentation | v3 Library source code | v3 Package (NPM) | v3 Samples

安装 @azure/ai-text-analytics NPM 包：Install the @azure/ai-text-analytics NPM packages:

npm install --save @azure/ai-text-analytics@5.1.0-beta.5

安装 @azure/ai-text-analytics NPM 包：Install the @azure/ai-text-analytics NPM packages:

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

"use strict";

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

"use strict";

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

创建一个新的 TextAnalyticsClient 对象并使用你的密钥和终结点作为参数。Create a new TextAnalyticsClient object with your key and endpoint as parameters.

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

创建一个新的 TextAnalyticsClient 对象并使用你的密钥和终结点作为参数。Create a new TextAnalyticsClient object with your key and endpoint as parameters.

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

创建一个字符串数组，使其包含要分析的文档。Create an array of strings containing the document you want to analyze. 调用客户端的 analyzeSentiment() 方法，并获取返回的 SentimentBatchResult 对象。Call the client's analyzeSentiment() method and get the returned SentimentBatchResult object. 循环访问结果列表，输出每个文档的 ID、文档级别情绪以及置信度分数。Iterate through the list of results, and print each document's ID, document level sentiment with confidence scores. 对于每个文档，结果都包含句子级别情绪以及偏移量、长度和置信度分数。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)

在控制台窗口中使用 node index.js 运行代码。Run your code with node index.js in your console window.

输出Output

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

观点挖掘Opinion mining

若要使用观点挖掘进行情绪分析，请创建一个包含要分析的文档的字符串数组。In order to do sentiment analysis with opinion mining, create an array of strings containing the document you want to analyze. 调用客户端的 analyzeSentiment() 方法（添加了选项标志 includeOpinionMining: true），并获取返回的 SentimentBatchResult 对象。Call the client's analyzeSentiment() method with adding option flag includeOpinionMining: true and get the returned SentimentBatchResult object. 循环访问结果列表，输出每个文档的 ID、文档级别情绪以及置信度分数。Iterate through the list of results, and print each document's ID, document level sentiment with confidence scores. 对于每个文档，结果不仅包含如上所述的句子级情绪，而且还包含角度和观点级情绪。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 results = await client.analyzeSentiment(sentimentInput, { includeOpinionMining: true });

  for (let i = 0; i < results.length; i++) {
    const result = results[i];
    console.log(`- Document ${result.id}`);
    if (!result.error) {
      console.log(`\tDocument text: ${sentimentInput[i].text}`);
      console.log(`\tOverall Sentiment: ${result.sentiment}`);
      console.log("\tSentiment confidence scores:", result.confidenceScores);
      console.log("\tSentences");
      for (const { sentiment, confidenceScores, opinions } of result.sentences) {
        console.log(`\t- Sentence sentiment: ${sentiment}`);
        console.log("\t  Confidence scores:", confidenceScores);
        console.log("\t  Mined opinions");
        for (const { target, assessments } of opinions) {
          console.log(`\t\t- Target text: ${target.text}`);
          console.log(`\t\t  Target sentiment: ${target.sentiment}`);
          console.log("\t\t  Target confidence scores:", target.confidenceScores);
          console.log("\t\t  Target assessments");
          for (const { text, sentiment } of assessments) {
            console.log(`\t\t\t- Text: ${text}`);
            console.log(`\t\t\t  Sentiment: ${sentiment}`);
          }
        }
      }
    } else {
      console.error(`\tError: ${result.error}`);
    }
  }
}
sentimentAnalysisWithOpinionMining(textAnalyticsClient)

在控制台窗口中使用 node index.js 运行代码。Run your code with node index.js in your console window.

输出Output

- Document 0
        Document text: The food and service were unacceptable, but the concierge were nice
        Overall Sentiment: positive
        Sentiment confidence scores: { positive: 0.84, neutral: 0, negative: 0.16 }
        Sentences
        - Sentence sentiment: positive
          Confidence scores: { positive: 0.84, neutral: 0, negative: 0.16 }
          Mined opinions
                - Target text: food
                  Target sentiment: negative
                  Target confidence scores: { positive: 0.01, negative: 0.99 }
                  Target assessments
                        - Text: unacceptable
                          Sentiment: negative
                - Target text: service
                  Target sentiment: negative
                  Target confidence scores: { positive: 0.01, negative: 0.99 }
                  Target assessments
                        - Text: unacceptable
                          Sentiment: negative
                - Target text: concierge
                  Target sentiment: positive
                  Target confidence scores: { positive: 1, negative: 0 }
                  Target assessments
                        - Text: nice
                          Sentiment: positive

创建一个字符串数组，使其包含要分析的文档。Create an array of strings containing the document you want to analyze. 调用客户端的 analyzeSentiment() 方法，并获取返回的 SentimentBatchResult 对象。Call the client's analyzeSentiment() method and get the returned SentimentBatchResult object. 循环访问结果列表，输出每个文档的 ID、文档级别情绪以及置信度分数。Iterate through the list of results, and print each document's ID, document level sentiment with confidence scores. 对于每个文档，结果都包含句子级别情绪以及偏移量、长度和置信度分数。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)

在控制台窗口中使用 node index.js 运行代码。Run your code with node index.js in your console window.

输出Output

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

创建一个字符串数组，使其包含要分析的文档。Create an array of strings containing the document you want to analyze. 调用客户端的 detectLanguage() 方法，并获取返回的 DetectLanguageResultCollection。Call the client's detectLanguage() method and get the returned DetectLanguageResultCollection. 然后循环访问结果，输出每个文档的 ID 以及各自的主要语言。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);

在控制台窗口中使用 node index.js 运行代码。Run your code with node index.js in your console window.

输出Output

ID: 0
        Primary Language French

创建一个字符串数组，使其包含要分析的文档。Create an array of strings containing the document you want to analyze. 调用客户端的 detectLanguage() 方法，并获取返回的 DetectLanguageResultCollection。Call the client's detectLanguage() method and get the returned DetectLanguageResultCollection. 然后循环访问结果，输出每个文档的 ID 以及各自的主要语言。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);

在控制台窗口中使用 node index.js 运行代码。Run your code with node index.js in your console window.

输出Output

ID: 0
        Primary Language French

创建一个字符串数组，使其包含要分析的文档。Create an array of strings containing the document you want to analyze. 调用客户端的 recognizeEntities() 方法，并获取 RecognizeEntitiesResult 对象。Call the client's recognizeEntities() method and get the RecognizeEntitiesResult object. 循环访问结果列表，并输出实体名称、类型、子类型、偏移量、长度和分数。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);

在控制台窗口中使用 node index.js 运行代码。Run your code with node index.js in your console window.

输出Output

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

实体链接Entity Linking

创建一个字符串数组，使其包含要分析的文档。Create an array of strings containing the document you want to analyze. 调用客户端的 recognizeLinkedEntities() 方法，并获取 RecognizeLinkedEntitiesResult 对象。Call the client's recognizeLinkedEntities() method and get the RecognizeLinkedEntitiesResult object. 循环访问结果列表，并输出实体名称、ID、数据源、URL 和匹配项。Iterate through the list of results, and print the entity name, ID, data source, url, and matches. matches 数组中的每个对象都将包含该匹配项的偏移量、长度和分数。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);

在控制台窗口中使用 node index.js 运行代码。Run your code with node index.js in your console window.

输出Output

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

个人身份信息 (PII) 识别Personally Identifying Information (PII) Recognition

创建一个字符串数组，使其包含要分析的文档。Create an array of strings containing the document you want to analyze. 调用客户端的 recognizePiiEntities() 方法，并获取 RecognizePIIEntitiesResult 对象。Call the client's recognizePiiEntities() method and get the RecognizePIIEntitiesResult object. 循环访问结果列表，并输出实体名称、类型和分数。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)

在控制台窗口中使用 node index.js 运行代码。Run your code with node index.js in your console window.

输出Output

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

创建一个字符串数组，使其包含要分析的文档。Create an array of strings containing the document you want to analyze. 调用客户端的 recognizeEntities() 方法，并获取 RecognizeEntitiesResult 对象。Call the client's recognizeEntities() method and get the RecognizeEntitiesResult object. 循环访问结果列表，并输出实体名称、类型、子类型、偏移量、长度和分数。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);

在控制台窗口中使用 node index.js 运行代码。Run your code with node index.js in your console window.

输出Output

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

实体链接Entity Linking

创建一个字符串数组，使其包含要分析的文档。Create an array of strings containing the document you want to analyze. 调用客户端的 recognizeLinkedEntities() 方法，并获取 RecognizeLinkedEntitiesResult 对象。Call the client's recognizeLinkedEntities() method and get the RecognizeLinkedEntitiesResult object. 循环访问结果列表，并输出实体名称、ID、数据源、URL 和匹配项。Iterate through the list of results, and print the entity name, ID, data source, url, and matches. matches 数组中的每个对象都将包含该匹配项的偏移量、长度和分数。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);

在控制台窗口中使用 node index.js 运行代码。Run your code with node index.js in your console window.

输出Output

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

创建一个字符串数组，使其包含要分析的文档。Create an array of strings containing the document you want to analyze. 调用客户端的 extractKeyPhrases() 方法，并获取返回的 ExtractKeyPhrasesResult 对象。Call the client's extractKeyPhrases() method and get the returned ExtractKeyPhrasesResult object. 循环访问结果，输出每个文档的 ID 以及任何检测到的密钥短语。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);

在控制台窗口中使用 node index.js 运行代码。Run your code with node index.js in your console window.

输出Output

ID: 0
        Document Key Phrases: cat,veterinarian

创建一个字符串数组，使其包含要分析的文档。Create an array of strings containing the document you want to analyze. 调用客户端的 extractKeyPhrases() 方法，并获取返回的 ExtractKeyPhrasesResult 对象。Call the client's extractKeyPhrases() method and get the returned ExtractKeyPhrasesResult object. 循环访问结果，输出每个文档的 ID 以及任何检测到的密钥短语。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);

在控制台窗口中使用 node index.js 运行代码。Run your code with node index.js in your console window.

输出Output

ID: 0
        Document Key Phrases: cat,veterinarian

v3.1 参考文档 | v3.1 库源代码 | v3.1 包 (PiPy) | v3.1 示例v3.1 Reference documentation | v3.1 Library source code | v3.1 Package (PiPy) | v3.1 Samples

v3 参考文档 | v3 库源代码 | v3 包(PiPy) | v3 示例v3 Reference documentation | v3 Library source code | v3 Package (PiPy) | v3 Samples

pip install azure-ai-textanalytics --pre

pip install --upgrade azure-ai-textanalytics

文本分析客户端是向 Azure 进行身份验证的 TextAnalyticsClient 对象。The Text Analytics client is a TextAnalyticsClient object that authenticates to Azure. 该客户端提供了几种方法来分析文本。The client provides several methods for analyzing text.

将处理文本以 documents 列表的形式发送到 API 时，该列表可以是字符串列表、类似于字典的表示形式列表或 TextDocumentInput/DetectLanguageInput 列表。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. dict-like 对象包含 id、text 和 language/country_hint 的组合。A dict-like object contains a combination of id, text, and language/country_hint. text 属性存储要以源 country_hint 分析的文本，而 id 则可以是任何值。The text attribute stores the text to be analyzed in the origin country_hint, and the id can be any value.

响应对象是一个列表，其中包含每个文档的分析信息。The response object is a list containing the analysis information for each document.

文本分析客户端是一个 TextAnalyticsClient 对象，它使用你的密钥向 Azure 进行身份验证。The Text Analytics client is a TextAnalyticsClient object that authenticates to Azure using your key. 该客户端提供了几种方法来成批分析文本。The client provides several methods for analyzing text as a batch.

当成批处理时，文本将以 documents 的列表的形式发送到 API，该项是包含 id、text 和 language 属性的组合的 dictionary 对象，具体取决于所用的方法。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. text 属性存储要以源 language 分析的文本，而 id 则可以是任何值。The text attribute stores the text to be analyzed in the origin language, and the id can be any value.

响应对象是一个列表，其中包含每个文档的已分析信息。The response object is a list containing the analyzed information for each document.

• 对客户端进行身份验证Authenticate the client
• 情绪分析Sentiment Analysis
• 观点挖掘Opinion mining
• 语言检测Language detection
• 命名实体识别Named Entity recognition
• 个人身份信息识别Personally Identifiable Information recognition
• 实体链接Entity linking
• 关键短语提取Key phrase extraction

• 对客户端进行身份验证Authenticate the client
• 情绪分析Sentiment Analysis
• 语言检测Language detection
• 命名实体识别Named Entity recognition
• 实体链接Entity linking
• 关键短语提取Key phrase extraction

创建一个函数，以便通过上面创建的 key 和 endpoint 来实例化 TextAnalyticsClient 对象。Create a function to instantiate the TextAnalyticsClient object with your key AND endpoint created above. 然后创建一个新客户端。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()

创建一个函数，以便通过上面创建的 key 和 endpoint 来实例化 TextAnalyticsClient 对象。Create a function to instantiate the TextAnalyticsClient object with your key AND endpoint created above. 然后创建一个新客户端。Then create a new client. 请注意，应定义 api_version=TextAnalyticsApiVersion.V3_0 以便使用版本 3.0。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()

如果使用 pip install azure-ai-textanalytics --pre 安装了 v5.1.0 版的客户端库，可使用客户端的 api_version 参数指定 v3.0 版的文本分析 API。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. 如果客户端是 v5.1.0 或更高版本，请仅使用以下 authenticate_client() 方法。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()

创建一个名为 sentiment_analysis_example() 的新函数，该函数采用客户端作为参数，然后调用 analyze_sentiment() 函数。Create a new function called sentiment_analysis_example() that takes the client as an argument, then calls the analyze_sentiment() function. 返回的响应对象将包含整个输入文档的情绪标签和分数，以及每个句子的情绪分析。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)

输出Output

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

观点挖掘Opinion mining

若要使用观点挖掘进行情绪分析，请创建一个名为 sentiment_analysis_with_opinion_mining_example() 的新函数（采用客户端作为参数），然后使用选项标志 show_opinion_mining=True 调用 analyze_sentiment() 函数。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. 返回的响应对象不仅包含整个输入文档的情绪标签和分数以及每个句子的情绪分析，还包含角度和观点级情绪分析。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:
                target = mined_opinion.target
                print("......'{}' target '{}'".format(target.sentiment, target.text))
                print("......Target score:\n......Positive={0:.2f}\n......Negative={1:.2f}\n".format(
                    target.confidence_scores.positive,
                    target.confidence_scores.negative,
                ))
                for assessment in mined_opinion.assessments:
                    print("......'{}' assessment '{}'".format(assessment.sentiment, assessment.text))
                    print("......Assessment score:\n......Positive={0:.2f}\n......Negative={1:.2f}\n".format(
                        assessment.confidence_scores.positive,
                        assessment.confidence_scores.negative,
                    ))
            print("\n")
        print("\n")
          
sentiment_analysis_with_opinion_mining_example(client)

输出Output

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' target 'food'
......Target score:
......Positive=0.01
......Negative=0.99

......'negative' assessment 'unacceptable'
......Assessment score:
......Positive=0.01
......Negative=0.99

......'negative' target 'service'
......Target score:
......Positive=0.01
......Negative=0.99

......'negative' assessment 'unacceptable'
......Assessment score:
......Positive=0.01
......Negative=0.99

......'positive' target 'concierge'
......Target score:
......Positive=1.00
......Negative=0.00

......'positive' assessment 'nice'
......Assessment score:
......Positive=1.00
......Negative=0.00





Press any key to continue . . .

创建一个名为 sentiment_analysis_example() 的新函数，该函数采用客户端作为参数，然后调用 analyze_sentiment() 函数。Create a new function called sentiment_analysis_example() that takes the client as an argument, then calls the analyze_sentiment() function. 返回的响应对象将包含整个输入文档的情绪标签和分数，以及每个句子的情绪分析。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)

输出Output

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

创建一个名为 language_detection_example() 的新函数，该函数采用客户端作为参数，然后调用 detect_language() 函数。Create a new function called language_detection_example() that takes the client as an argument, then calls the detect_language() function. 如果成功，则返回的响应对象将在 primary_language 中包含检测到的语言，否则将包含 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)

输出Output

Language:  French

创建一个名为 language_detection_example() 的新函数，该函数采用客户端作为参数，然后调用 detect_language() 函数。Create a new function called language_detection_example() that takes the client as an argument, then calls the detect_language() function. 如果成功，则返回的响应对象将在 primary_language 中包含检测到的语言，否则将包含 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)

输出Output

Language:  French

创建一个名为 entity_recognition_example 的新函数，该函数采用客户端作为参数，然后调用 recognize_entities() 函数并循环访问结果。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. 如果成功，则返回的响应对象将在 entity 中包含检测到的实体列表，否则将包含 error。The returned response object will contain the list of detected entities in entity if successful, and an error if not. 对于检测到的每个实体，输出其类别和子类别（如果存在）。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)

输出Output

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

实体链接Entity Linking

创建一个名为 entity_linking_example() 的新函数，该函数采用客户端作为参数，然后调用 recognize_linked_entities() 函数并循环访问结果。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. 如果成功，则返回的响应对象将在 entities 中包含检测到的实体列表，否则将包含 error。The returned response object will contain the list of detected entities in entities if successful, and an error if not. 由于链接实体是唯一标识的，因此同一实体的实例将以分组形式出现在 entity 对象下，显示为 match 对象的列表。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)

输出Output

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

个人身份信息识别Personally Identifiable Information recognition

创建一个名为 pii_recognition_example 的新函数，该函数采用客户端作为参数，然后调用 recognize_pii_entities() 函数并循环访问结果。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. 如果成功，则返回的响应对象将在 entity 中包含检测到的实体列表，否则将包含 error。The returned response object will contain the list of detected entities in entity if successful, and an error if not. 对于检测到的每个实体，输出其类别和子类别（如果存在）。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)

输出Output

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

创建一个名为 entity_recognition_example 的新函数，该函数采用客户端作为参数，然后调用 recognize_entities() 函数并循环访问结果。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. 如果成功，则返回的响应对象将在 entity 中包含检测到的实体列表，否则将包含 error。The returned response object will contain the list of detected entities in entity if successful, and an error if not. 对于检测到的每个实体，输出其类别和子类别（如果存在）。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)

输出Output

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

实体链接Entity Linking

创建一个名为 entity_linking_example() 的新函数，该函数采用客户端作为参数，然后调用 recognize_linked_entities() 函数并循环访问结果。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. 如果成功，则返回的响应对象将在 entities 中包含检测到的实体列表，否则将包含 error。The returned response object will contain the list of detected entities in entities if successful, and an error if not. 由于链接实体是唯一标识的，因此同一实体的实例将以分组形式出现在 entity 对象下，显示为 match 对象的列表。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)

输出Output

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

创建一个名为 key_phrase_extraction_example() 的新函数，该函数采用客户端作为参数，然后调用 extract_key_phrases() 函数。Create a new function called key_phrase_extraction_example() that takes the client as an argument, then calls the extract_key_phrases() function. 如果成功，结果将包含 key_phrases 中检测到的关键短语列表，如果失败，则将包含 error。The result will contain the list of detected key phrases in key_phrases if successful, and an error if not. 输出任何检测到的关键短语。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)

输出Output

    Key Phrases:
         cat
         veterinarian

创建一个名为 key_phrase_extraction_example() 的新函数，该函数采用客户端作为参数，然后调用 extract_key_phrases() 函数。Create a new function called key_phrase_extraction_example() that takes the client as an argument, then calls the extract_key_phrases() function. 如果成功，结果将包含 key_phrases 中检测到的关键短语列表，如果失败，则将包含 error。The result will contain the list of detected key phrases in key_phrases if successful, and an error if not. 输出任何检测到的关键短语。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)

输出Output

    Key Phrases:
         cat
         veterinarian

v3.1 参考文档v3.1 Reference documentation

v3 参考文档v3 Reference documentation

curl -X POST https://<your-text-analytics-endpoint-here>/text/analytics/v3.1-preview.4/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 响应JSON 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.",
               "targets":[
                  {
                     "sentiment":"positive",
                     "confidenceScores":{
                        "positive":1.0,
                        "negative":0.0
                     },
                     "offset":4,
                     "length":16,
                     "text":"customer service",
                     "relations":[
                        {
                           "relationType":"assessment",
                           "ref":"#/documents/0/sentences/0/assessments/0"
                        }
                     ]
                  }
               ],
               "assessments":[
                  {
                     "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 响应JSON 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.4/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 响应JSON response

{
   "documents":[
      {
         "id":"1",
         "detectedLanguage":{
            "name":"English",
            "iso6391Name":"en",
            "confidenceScore":1.0
         },
         "warnings":[
            
         ]
      }
   ],
   "errors":[
      
   ],
   "modelVersion":"2021-01-05"
}

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 响应JSON 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.4/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 响应JSON response

{
   "documents":[
      {
         "id":"1",
         "entities":[
            {
               "text":"Seattle",
               "category":"Location",
               "subcategory":"GPE",
               "offset":26,
               "length":7,
               "confidenceScore":0.99
            },
            {
               "text":"last week",
               "category":"DateTime",
               "subcategory":"DateRange",
               "offset":34,
               "length":9,
               "confidenceScore":0.8
            }
         ],
         "warnings":[
            
         ]
      }
   ],
   "errors":[
      
   ],
   "modelVersion":"2021-01-15"
}

检测个人身份信息Detecting personally identifying information

1. 将命令复制到文本编辑器中。Copy the command into a text editor.
2. 必要时在命令中进行如下更改：Make the following changes in the command where needed:
   1. 将值 <your-text-analytics-key-here> 替换为你的值。Replace the value <your-text-analytics-key-here> with your key.
   2. 将请求 URL 的第一部分 (<your-text-analytics-endpoint-here>) 替换为你自己的终结点 URL。Replace the first part of the request URL <your-text-analytics-endpoint-here> with the your own endpoint URL.
3. 打开命令提示符窗口。Open a command prompt window.
4. 将文本编辑器中的命令粘贴到命令提示符窗口，然后运行命令。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.4/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 响应JSON response

{
   "documents":[
      {
         "redactedText":"Call our office at ************, or send an email to *******************",
         "id":"1",
         "entities":[
            {
               "text":"312-555-1234",
               "category":"PhoneNumber",
               "offset":19,
               "length":12,
               "confidenceScore":0.8
            },
            {
               "text":"support@contoso.com",
               "category":"Email",
               "offset":53,
               "length":19,
               "confidenceScore":0.8
            }
         ],
         "warnings":[
            
         ]
      }
   ],
   "errors":[
      
   ],
   "modelVersion":"2021-01-15"
}

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 响应JSON 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.4/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 响应JSON 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 响应JSON 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.4/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."}]}'

{
   "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 响应JSON response

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