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    Classifying Content

    Content Classificationanalyzes a document and returns a list of content categories that apply to the text found in the document. To classify the content in a document, call the classifyText method.

    A complete list of content categories returned for the classifyText method are found here .

    You can choose which model to use for the classifyText method by setting the optional classificationModelOptions field:

    This section demonstrates how to classify content in a document. For each document, you must submit a separate request.

    Classifying Content

    Here is an example of classifying content provided as a string:

    Protocol

    To classify content from a document, make a POST request to the documents:classifyText REST method and provide the appropriate request body as shown in the following example.

    The example uses the gcloud auth application-default print-access-token command to obtain an access token for a service account set up for the project using the Google Cloud Platform gcloud CLI . For instructions on installing the gcloud CLI, setting up a project with a service account see the Quickstart .

    curl  
-X  
POST  
 \ 
  
-H  
 "Authorization: Bearer " 
 $( 
gcloud  
auth  
application-default  
print-access-token ) 
  
 \ 
  
-H  
 "Content-Type: application/json; charset=utf-8" 
  
 \ 
  
--data  
 "{ 
 'document':{ 
 'type':'PLAIN_TEXT', 
 'content':'Google, headquartered in Mountain View, unveiled the new Android 
 phone at the Consumer Electronic Show.  Sundar Pichai said in his keynote 
 that users love their new Android phones.' 
 }, 
 'classificationModelOptions': { 
 'v2Model': { 
 'contentCategoriesVersion': 'V2', 
 } 
 } 
 }" 
  
 "https://language.googleapis.com/v1/documents:classifyText"

    Go

    To learn how to install and use the client library for Natural Language, see Natural Language client libraries . For more information, see the Natural Language Go API reference documentation .

    To authenticate to Natural Language, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

      func 
  
 classifyText 
 ( 
 ctx 
  
 context 
 . 
 Context 
 , 
  
 client 
  
 * 
 language 
 . 
 Client 
 , 
  
 text 
  
 string 
 ) 
  
 ( 
 * 
 languagepb 
 . 
 ClassifyTextResponse 
 , 
  
 error 
 ) 
  
 { 
  
 return 
  
 client 
 . 
 ClassifyText 
 ( 
 ctx 
 , 
  
& languagepb 
 . 
 ClassifyTextRequest 
 { 
  
 Document 
 : 
  
& languagepb 
 . 
 Document 
 { 
  
 Source 
 : 
  
& languagepb 
 . 
 Document_Content 
 { 
  
 Content 
 : 
  
 text 
 , 
  
 }, 
  
 Type 
 : 
  
 languagepb 
 . 
 Document_PLAIN_TEXT 
 , 
  
 }, 
  
 ClassificationModelOptions 
 : 
  
& languagepb 
 . 
 ClassificationModelOptions 
 { 
  
 ModelType 
 : 
  
& languagepb 
 . 
 ClassificationModelOptions_V2Model_ 
 { 
  
 V2Model 
 : 
  
& languagepb 
 . 
 ClassificationModelOptions_V2Model 
 { 
  
 ContentCategoriesVersion 
 : 
  
 languagepb 
 . 
 ClassificationModelOptions_V2Model_V2 
 , 
  
 }, 
  
 }, 
  
 }, 
  
 }) 
 }

    Java

    To learn how to install and use the client library for Natural Language, see Natural Language client libraries . For more information, see the Natural Language Java API reference documentation .

    To authenticate to Natural Language, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

      // Instantiate the Language client com.google.cloud.language.v2.LanguageServiceClient 
 try 
  
 ( 
 LanguageServiceClient 
  
 language 
  
 = 
  
 LanguageServiceClient 
 . 
 create 
 ()) 
  
 { 
  
 // Set content to the text string 
  
 Document 
  
 doc 
  
 = 
  
 Document 
 . 
 newBuilder 
 (). 
 setContent 
 ( 
 text 
 ). 
 setType 
 ( 
 Type 
 . 
 PLAIN_TEXT 
 ). 
 build 
 (); 
  
 ClassifyTextRequest 
  
 request 
  
 = 
  
 ClassifyTextRequest 
 . 
 newBuilder 
 (). 
 setDocument 
 ( 
 doc 
 ). 
 build 
 (); 
  
 // Detect categories in the given text 
  
 ClassifyTextResponse 
  
 response 
  
 = 
  
 language 
 . 
 classifyText 
 ( 
 request 
 ); 
  
 for 
  
 ( 
 ClassificationCategory 
  
 category 
  
 : 
  
 response 
 . 
 getCategoriesList 
 ()) 
  
 { 
  
 System 
 . 
 out 
 . 
 printf 
 ( 
  
 "Category name : %s, Confidence : %.3f\n" 
 , 
  
 category 
 . 
 getName 
 (), 
  
 category 
 . 
 getConfidence 
 ()); 
  
 } 
 }

    Node.js

    To learn how to install and use the client library for Natural Language, see Natural Language client libraries . For more information, see the Natural Language Node.js API reference documentation .

    To authenticate to Natural Language, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

      // Imports the Google Cloud client library 
 const 
  
 language 
  
 = 
  
 require 
 ( 
 ' @google-cloud/language 
' 
 ); 
 // Creates a client 
 const 
  
 client 
  
 = 
  
 new 
  
 language 
 . 
  LanguageServiceClient 
 
 (); 
 /** 
 * TODO(developer): Uncomment the following line to run this code. 
 */ 
 // const text = 'Your text to analyze, e.g. Hello, world!'; 
 // Prepares a document, representing the provided text 
 const 
  
 document 
  
 = 
  
 { 
  
 content 
 : 
  
 text 
 , 
  
 type 
 : 
  
 'PLAIN_TEXT' 
 , 
 }; 
 const 
  
 classificationModelOptions 
  
 = 
  
 { 
  
 v2Model 
 : 
  
 { 
  
 contentCategoriesVersion 
 : 
  
 'V2' 
 , 
  
 }, 
 }; 
 // Classifies text in the document 
 const 
  
 [ 
 classification 
 ] 
  
 = 
  
 await 
  
 client 
 . 
 classifyText 
 ({ 
  
 document 
 , 
  
 classificationModelOptions 
 , 
 }); 
 console 
 . 
 log 
 ( 
 'Categories:' 
 ); 
 classification 
 . 
 categories 
 . 
 forEach 
 ( 
 category 
  
 = 
>  
 { 
  
 console 
 . 
 log 
 ( 
 `Name: 
 ${ 
 category 
 . 
 name 
 } 
 , Confidence: 
 ${ 
 category 
 . 
 confidence 
 } 
 ` 
 ); 
 });

    Python

    To learn how to install and use the client library for Natural Language, see Natural Language client libraries . For more information, see the Natural Language Python API reference documentation .

    To authenticate to Natural Language, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

      from 
  
 google.cloud 
  
 import 
 language_v1 
 def 
  
 sample_classify_text 
 ( 
 text_content 
 ): 
  
 """ 
 Classifying Content in a String 
 Args: 
 text_content The text content to analyze. 
 """ 
 client 
 = 
 language_v1 
 . 
 LanguageServiceClient 
 () 
 # text_content = "That actor on TV makes movies in Hollywood and also stars in a variety of popular new TV shows." 
 # Available types: PLAIN_TEXT, HTML 
 type_ 
 = 
 language_v1 
 . 
 Document 
 . 
 Type 
 . 
 PLAIN_TEXT 
 # Optional. If not specified, the language is automatically detected. 
 # For list of supported languages: 
 # https://cloud.google.com/natural-language/docs/languages 
 language 
 = 
 "en" 
 document 
 = 
 { 
 "content" 
 : 
 text_content 
 , 
 "type_" 
 : 
 type_ 
 , 
 "language" 
 : 
 language 
 } 
 content_categories_version 
 = 
 ( 
 language_v1 
 . 
  ClassificationModelOptions 
 
 . 
  V2Model 
 
 . 
  ContentCategoriesVersion 
 
 . 
 V2 
 ) 
 response 
 = 
 client 
 . 
  classify_text 
 
 ( 
 request 
 = 
 { 
 "document" 
 : 
 document 
 , 
 "classification_model_options" 
 : 
 { 
 "v2_model" 
 : 
 { 
 "content_categories_version" 
 : 
 content_categories_version 
 } 
 }, 
 } 
 ) 
 # Loop through classified categories returned from the API 
 for 
 category 
 in 
 response 
 . 
 categories 
 : 
 # Get the name of the category representing the document. 
 # See the predefined taxonomy of categories: 
 # https://cloud.google.com/natural-language/docs/categories 
 print 
 ( 
 f 
 "Category name: 
 { 
 category 
 . 
 name 
 } 
 " 
 ) 
 # Get the confidence. Number representing how certain the classifier 
 # is that this category represents the provided text. 
 print 
 ( 
 f 
 "Confidence: 
 { 
 category 
 . 
 confidence 
 } 
 " 
 )

    Additional languages

    C#: Please follow the C# setup instructions on the client libraries page and then visit the Natural Language reference documentation for .NET.

    PHP: Please follow the PHP setup instructions on the client libraries page and then visit the Natural Language reference documentation for PHP.

    Ruby: Please follow the Ruby setup instructions on the client libraries page and then visit the Natural Language reference documentation for Ruby.

    Classifying Content from Cloud Storage

    Here is an example of classifying content stored in a text file on Cloud Storage:

    Protocol

    To classify content from a document stored in Cloud Storage, make a POST request to the documents:classifyText REST method and provide the appropriate request body with the path to the document as shown in the following example.

    curl  
-X  
POST  
 \ 
  
-H  
 "Authorization: Bearer " 
 $( 
gcloud  
auth  
application-default  
print-access-token ) 
  
 \ 
  
-H  
 "Content-Type: application/json; charset=utf-8" 
  
 \ 
  
--data  
 "{ 
 'document':{ 
 'type':'PLAIN_TEXT', 
 'gcsContentUri':'gs:// <bucket-name> 
/ <object-name> 
' 
 } 
 'classificationModelOptions': { 
 'v1Model': { 
 } 
 } 
 }" 
  
 "https://language.googleapis.com/v1/documents:classifyText"

    Go

    To learn how to install and use the client library for Natural Language, see Natural Language client libraries . For more information, see the Natural Language Go API reference documentation .

    To authenticate to Natural Language, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

      func 
  
 classifyTextFromGCS 
 ( 
 ctx 
  
 context 
 . 
 Context 
 , 
  
 gcsURI 
  
 string 
 ) 
  
 ( 
 * 
 languagepb 
 . 
 ClassifyTextResponse 
 , 
  
 error 
 ) 
  
 { 
  
 return 
  
 client 
 . 
 ClassifyText 
 ( 
 ctx 
 , 
  
& languagepb 
 . 
 ClassifyTextRequest 
 { 
  
 Document 
 : 
  
& languagepb 
 . 
 Document 
 { 
  
 Source 
 : 
  
& languagepb 
 . 
 Document_GcsContentUri 
 { 
  
 GcsContentUri 
 : 
  
 gcsURI 
 , 
  
 }, 
  
 Type 
 : 
  
 languagepb 
 . 
 Document_PLAIN_TEXT 
 , 
  
 }, 
  
 }) 
 }

    Java

    To learn how to install and use the client library for Natural Language, see Natural Language client libraries . For more information, see the Natural Language Java API reference documentation .

    To authenticate to Natural Language, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

      // Instantiate the Language client com.google.cloud.language.v2.LanguageServiceClient 
 try 
  
 ( 
 LanguageServiceClient 
  
 language 
  
 = 
  
 LanguageServiceClient 
 . 
 create 
 ()) 
  
 { 
  
 // Set the GCS content URI path 
  
 Document 
  
 doc 
  
 = 
  
 Document 
 . 
 newBuilder 
 (). 
 setGcsContentUri 
 ( 
 gcsUri 
 ). 
 setType 
 ( 
 Type 
 . 
 PLAIN_TEXT 
 ). 
 build 
 (); 
  
 ClassifyTextRequest 
  
 request 
  
 = 
  
 ClassifyTextRequest 
 . 
 newBuilder 
 (). 
 setDocument 
 ( 
 doc 
 ). 
 build 
 (); 
  
 // Detect categories in the given file 
  
 ClassifyTextResponse 
  
 response 
  
 = 
  
 language 
 . 
 classifyText 
 ( 
 request 
 ); 
  
 for 
  
 ( 
 ClassificationCategory 
  
 category 
  
 : 
  
 response 
 . 
 getCategoriesList 
 ()) 
  
 { 
  
 System 
 . 
 out 
 . 
 printf 
 ( 
  
 "Category name : %s, Confidence : %.3f\n" 
 , 
  
 category 
 . 
 getName 
 (), 
  
 category 
 . 
 getConfidence 
 ()); 
  
 } 
 }

    Node.js

    To learn how to install and use the client library for Natural Language, see Natural Language client libraries . For more information, see the Natural Language Node.js API reference documentation .

    To authenticate to Natural Language, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

      // Imports the Google Cloud client library. 
 const 
  
 language 
  
 = 
  
 require 
 ( 
 ' @google-cloud/language 
' 
 ); 
 // Creates a client. 
 const 
  
 client 
  
 = 
  
 new 
  
 language 
 . 
  LanguageServiceClient 
 
 (); 
 /** 
 * TODO(developer): Uncomment the following lines to run this code 
 */ 
 // const bucketName = 'Your bucket name, e.g. my-bucket'; 
 // const fileName = 'Your file name, e.g. my-file.txt'; 
 // Prepares a document, representing a text file in Cloud Storage 
 const 
  
 document 
  
 = 
  
 { 
  
 gcsContentUri 
 : 
  
 `gs:// 
 ${ 
 bucketName 
 } 
 / 
 ${ 
 fileName 
 } 
 ` 
 , 
  
 type 
 : 
  
 'PLAIN_TEXT' 
 , 
 }; 
 // Classifies text in the document 
 const 
  
 [ 
 classification 
 ] 
  
 = 
  
 await 
  
 client 
 . 
 classifyText 
 ({ 
 document 
 }); 
 console 
 . 
 log 
 ( 
 'Categories:' 
 ); 
 classification 
 . 
 categories 
 . 
 forEach 
 ( 
 category 
  
 = 
>  
 { 
  
 console 
 . 
 log 
 ( 
 `Name: 
 ${ 
 category 
 . 
 name 
 } 
 , Confidence: 
 ${ 
 category 
 . 
 confidence 
 } 
 ` 
 ); 
 });

    Python

    To learn how to install and use the client library for Natural Language, see Natural Language client libraries . For more information, see the Natural Language Python API reference documentation .

    To authenticate to Natural Language, set up Application Default Credentials. For more information, see Set up authentication for a local development environment .

      from 
  
 google.cloud 
  
 import 
 language_v1 
 def 
  
 sample_classify_text 
 ( 
 gcs_content_uri 
 ): 
  
 """ 
 Classifying Content in text file stored in Cloud Storage 
 Args: 
 gcs_content_uri Google Cloud Storage URI where the file content is located. 
 e.g. gs://[Your Bucket]/[Path to File] 
 The text file must include at least 20 words. 
 """ 
 client 
 = 
 language_v1 
 . 
 LanguageServiceClient 
 () 
 # gcs_content_uri = 'gs://cloud-samples-data/language/classify-entertainment.txt' 
 # Available types: PLAIN_TEXT, HTML 
 type_ 
 = 
 language_v1 
 . 
 Document 
 . 
 Type 
 . 
 PLAIN_TEXT 
 # Optional. If not specified, the language is automatically detected. 
 # For list of supported languages: 
 # https://cloud.google.com/natural-language/docs/languages 
 language 
 = 
 "en" 
 document 
 = 
 { 
 "gcs_content_uri" 
 : 
 gcs_content_uri 
 , 
 "type_" 
 : 
 type_ 
 , 
 "language" 
 : 
 language 
 , 
 } 
 response 
 = 
 client 
 . 
  classify_text 
 
 ( 
 request 
 = 
 { 
 "document" 
 : 
 document 
 }) 
 # Loop through classified categories returned from the API 
 for 
 category 
 in 
 response 
 . 
 categories 
 : 
 # Get the name of the category representing the document. 
 # See the predefined taxonomy of categories: 
 # https://cloud.google.com/natural-language/docs/categories 
 print 
 ( 
 f 
 "Category name: 
 { 
 category 
 . 
 name 
 } 
 " 
 ) 
 # Get the confidence. Number representing how certain the classifier 
 # is that this category represents the provided text. 
 print 
 ( 
 f 
 "Confidence: 
 { 
 category 
 . 
 confidence 
 } 
 " 
 )

    Additional languages

    C#: Please follow the C# setup instructions on the client libraries page and then visit the Natural Language reference documentation for .NET.

    PHP: Please follow the PHP setup instructions on the client libraries page and then visit the Natural Language reference documentation for PHP.

    Ruby: Please follow the Ruby setup instructions on the client libraries page and then visit the Natural Language reference documentation for Ruby.

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