Analyzing Sentiment

import ( "context" "fmt" "io" language "cloud.google.com/go/language/apiv2" "cloud.google.com/go/language/apiv2/languagepb" ) // analyzeSentiment sends a string of text to the Cloud Natural Language API to // assess the sentiment of the text. func analyzeSentiment(w io.Writer, text string) error { ctx := context.Background() // Initialize client. client, err := language.NewClient(ctx) if err != nil { return err } defer client.Close() resp, err := client.AnalyzeSentiment(ctx, &languagepb.AnalyzeSentimentRequest{ Document: &languagepb.Document{ Source: &languagepb.Document_Content{ Content: text, }, Type: languagepb.Document_PLAIN_TEXT, }, EncodingType: languagepb.EncodingType_UTF8, }) if err != nil { return fmt.Errorf("AnalyzeSentiment: %w", err) } fmt.Fprintf(w, "Response: %q\n", resp) return nil } 

// Instantiate the Language client com.google.cloud.language.v2.LanguageServiceClient try (LanguageServiceClient language = LanguageServiceClient.create()) {  Document doc = Document.newBuilder().setContent(text).setType(Type.PLAIN_TEXT).build();  AnalyzeSentimentResponse response = language.analyzeSentiment(doc);  Sentiment sentiment = response.getDocumentSentiment();  if (sentiment == null) {  System.out.println("No sentiment found");  } else {  System.out.printf("Sentiment magnitude: %.3f\n", sentiment.getMagnitude());  System.out.printf("Sentiment score: %.3f\n", sentiment.getScore());  }  return sentiment; }

from google.cloud import language_v2 def sample_analyze_sentiment(text_content: str = "I am so happy and joyful.") -> None:  """  Analyzes Sentiment in a string.  Args:  text_content: The text content to analyze.  """ client = language_v2.LanguageServiceClient() # text_content = 'I am so happy and joyful.' # Available types: PLAIN_TEXT, HTML document_type_in_plain_text = language_v2.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_code = "en" document = { "content": text_content, "type_": document_type_in_plain_text, "language_code": language_code, } # Available values: NONE, UTF8, UTF16, UTF32 # See https://cloud.google.com/natural-language/docs/reference/rest/v2/EncodingType. encoding_type = language_v2.EncodingType.UTF8 response = client.analyze_sentiment( request={"document": document, "encoding_type": encoding_type} ) # Get overall sentiment of the input document print(f"Document sentiment score: {response.document_sentiment.score}") print(f"Document sentiment magnitude: {response.document_sentiment.magnitude}") # Get sentiment for all sentences in the document for sentence in response.sentences: print(f"Sentence text: {sentence.text.content}") print(f"Sentence sentiment score: {sentence.sentiment.score}") print(f"Sentence sentiment magnitude: {sentence.sentiment.magnitude}") # Get the language of the text, which will be the same as # the language specified in the request or, if not specified, # the automatically-detected language. print(f"Language of the text: {response.language_code}") 

 func analyzeSentimentFromGCS(ctx context.Context, gcsURI string) (*languagepb.AnalyzeSentimentResponse, error) { return client.AnalyzeSentiment(ctx, &languagepb.AnalyzeSentimentRequest{ Document: &languagepb.Document{ Source: &languagepb.Document_GcsContentUri{ GcsContentUri: gcsURI, }, Type: languagepb.Document_PLAIN_TEXT, }, }) } 

// Instantiate the Language client com.google.cloud.language.v2.LanguageServiceClient try (LanguageServiceClient language = LanguageServiceClient.create()) {  Document doc =  Document.newBuilder().setGcsContentUri(gcsUri).setType(Type.PLAIN_TEXT).build();  AnalyzeSentimentResponse response = language.analyzeSentiment(doc);  Sentiment sentiment = response.getDocumentSentiment();  if (sentiment == null) {  System.out.println("No sentiment found");  } else {  System.out.printf("Sentiment magnitude : %.3f\n", sentiment.getMagnitude());  System.out.printf("Sentiment score : %.3f\n", sentiment.getScore());  }  return sentiment; }

// Imports the Google Cloud client library const language = require('@google-cloud/language').v2; // 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', }; // Detects the sentiment of the document const [result] = await client.analyzeSentiment({document}); const sentiment = result.documentSentiment; console.log('Document sentiment:'); console.log(` Score: ${sentiment.score}`); console.log(` Magnitude: ${sentiment.magnitude}`); const sentences = result.sentences; sentences.forEach(sentence => {  console.log(`Sentence: ${sentence.text.content}`);  console.log(` Score: ${sentence.sentiment.score}`);  console.log(` Magnitude: ${sentence.sentiment.magnitude}`); });

from google.cloud import language_v2 def sample_analyze_sentiment( gcs_content_uri: str = "gs://cloud-samples-data/language/sentiment-positive.txt", ) -> None:  """  Analyzes Sentiment 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]  """ client = language_v2.LanguageServiceClient() # Available types: PLAIN_TEXT, HTML document_type_in_plain_text = language_v2.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_code = "en" document = { "gcs_content_uri": gcs_content_uri, "type_": document_type_in_plain_text, "language_code": language_code, } # Available values: NONE, UTF8, UTF16, UTF32 # See https://cloud.google.com/natural-language/docs/reference/rest/v2/EncodingType. encoding_type = language_v2.EncodingType.UTF8 response = client.analyze_sentiment( request={"document": document, "encoding_type": encoding_type} ) # Get overall sentiment of the input document print(f"Document sentiment score: {response.document_sentiment.score}") print(f"Document sentiment magnitude: {response.document_sentiment.magnitude}") # Get sentiment for all sentences in the document for sentence in response.sentences: print(f"Sentence text: {sentence.text.content}") print(f"Sentence sentiment score: {sentence.sentiment.score}") print(f"Sentence sentiment magnitude: {sentence.sentiment.magnitude}") # Get the language of the text, which will be the same as # the language specified in the request or, if not specified, # the automatically-detected language. print(f"Language of the text: {response.language_code}")