{"id":28698,"date":"2019-04-30T00:00:00","date_gmt":"2019-04-29T22:00:00","guid":{"rendered":"https:\/\/blexin.com\/lost-in-translations-traduzione-e-sentiment-analysis-in-tempo-reale\/"},"modified":"2021-05-20T19:18:23","modified_gmt":"2021-05-20T17:18:23","slug":"lost-in-translations-translation-and-sentiment-analysis-in-real-time","status":"publish","type":"post","link":"https:\/\/blexin.com\/en\/blog-en\/lost-in-translations-translation-and-sentiment-analysis-in-real-time\/","title":{"rendered":"Lost in Translations: translation and Sentiment Analysis in real time"},"content":{"rendered":"\n
\"\"<\/figure>\n\n\n\n

Foreword<\/h2>\n\n\n\n

Let\u2019s go on with our journey in the cognitive services<\/strong> world (read the previous article<\/a>), This time, we start from an Angular<\/strong> project. We will use Google Translate API <\/strong>to translate the text inserted in a text-area in real-time, taking advantage of Angular Reactive Forms and we will undergo the same text to a Sentiment Analysis <\/strong>at once, thanks to the Text Analytics API<\/strong> in Azure<\/strong>.<\/p>\n\n\n\n

To use Google translation services, it is needed to create a project in GCP console, in which to enable the Cloud Translation API. In the project\u2019s configuration panel, it is possible to extract the key to insert in http calls. The documentation of doable REST calls is available to the following link<\/a>. Starting from it, we create two Typescript interfaces, in order to manage the request and the response.<\/p>\n\n\n\n

\n
\nexport interface GoogleTranslateRequest {\n\u00a0\u00a0q: string;\n\u00a0\u00a0source: string;\n\u00a0\u00a0target: string;\n\u00a0\u00a0format: string;\n}\n<\/pre><\/div><\/div>\n\n\n\n
\nexport interface Translation {\n\u00a0\u00a0translatedText: string;\n}\n\u00a0\nexport interface Data {\n\u00a0\u00a0translations: Translation[];\n}\n\u00a0\nexport interface GoogleTranslateResponse {\n\u00a0\u00a0data: Data;\n\u00a0\u00a0language: string;\n}\n<\/pre><\/div><\/div>\n<\/div>\n\n\n\n
We create then a service to run the REST call through the Angular HttpClientModule. It is important to note how the environment has been imported, to recover the service\u2019s URL and the key from the environment file. The class has a translate<\/em> method which returns an Observable<GoogleTranslateResponse><\/p>\n\n\n
\n@Injectable({\n\u00a0\u00a0providedIn: 'root'\n})\nexport class GoogleService {\n\u00a0\n\u00a0\u00a0url = environment.googleTranslateUrl + environment.googleApiKey;\n\u00a0\n\u00a0\u00a0constructor(private http: HttpClient) {}\n\u00a0\n\u00a0\u00a0translate(obj: GoogleTranslateRequest): Observable<GoogleTranslateResponse> {\n\u00a0\u00a0\u00a0\u00a0return this.http.post<GoogleTranslateResponse>(this.url, obj);\n\u00a0\u00a0}\n}\n<\/pre><\/div>\n\n\n
The Azure Text Analytics sentiment analysis service<\/h2>\n\n\n\n
From the Azure portal is possible to add a new resource of Artificial intelligence and Machine Learning type, Text Analyze<\/strong>. A URL and a key for the REST call will be linked to this resource.<\/p>\n\n\n\n
In this case too, we create an interface for the request and another one for the response<\/p>\n\n\n\n
\n
\nexport interface AzureSentimentRequest {\n\u00a0\u00a0documents: AzureSentimentDocument[];\n}\n\u00a0\nexport interface AzureSentimentDocument {\n\u00a0\u00a0language: string;\n\u00a0\u00a0id: number;\n\u00a0\u00a0text: string;\n}\n<\/pre><\/div><\/div>\n\n\n\n
\nexport interface Document {\n\u00a0\u00a0id: string;\n\u00a0\u00a0score: number;\n}\n\u00a0\nexport interface AzureSentimentResponse {\n\u00a0\u00a0documents: Document[];\n\u00a0\u00a0errors: any[];\n}\n<\/pre><\/div><\/div>\n<\/div>\n\n\n\n
We create then a service called Azureservice, that makes available a method called analyze. Languages and text to be analyzed should be passed to this method and it will return a Observable<AzureSentimentResponse>.<\/p>\n\n\n
\n@Injectable({\n\u00a0\u00a0providedIn: 'root'\n})\nexport class AzureService {\n\u00a0\n\u00a0\u00a0httpOptions = {\n\u00a0\u00a0\u00a0\u00a0headers: new HttpHeaders({ 'Ocp-Apim-Subscription-Key': environment.azureTextAnalyticsKey,\n\u00a0\u00a0\u00a0\u00a0'Content-Type': 'application\/json',\n\u00a0\u00a0\u00a0\u00a0Accept: 'application\/json'\n\u00a0\u00a0})};\n\u00a0\n\u00a0\u00a0constructor(private http: HttpClient) {}\n\u00a0\n\u00a0\u00a0analize(lang: string, textInput: string): Observable<AzureSentimentResponse> {\n\u00a0\n\u00a0\u00a0\u00a0\u00a0const body: AzureSentimentRequest = {\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0documents : [\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0{id: 1,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0language: lang,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0text: textInput\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0}]\n\u00a0\u00a0\u00a0\u00a0};\n\u00a0\u00a0\u00a0\u00a0return this.http.post<AzureSentimentResponse>(environment.azureTextAnalyticsUrl, body, this.httpOptions);\n\u00a0\u00a0}\n}\n<\/pre><\/div>\n\n\n
The component<\/h2>\n\n\n\n
In the app.module.ts we imported the ReactiveFormesModule, in order to make available, through an observable, the text to be analyzed originated from a textarea.<\/p>\n\n\n
\n<div class="form-group">\n\u00a0\u00a0<label for="testoOriginale">Testo da Tradurre<\/label>\n\u00a0\u00a0<textarea class="form-control" id="testoOriginale" rows="3" [formControl]='testoOriginale'><\/textarea>\n<\/div>\n<\/pre><\/div>\n\n\n
In the typescript code of the component, we can declare<\/p>\n\n\n
\ntestoIstantaneo: Observable<string> = null;\n<\/pre><\/div>\n\n\n
and then link to testoistantaneo the valueChanges of the formControl testoOriginale<\/p>\n\n\n
\nthis.testoIstantaneo = this.testoOriginale.valueChanges;\n<\/pre><\/div>\n\n\n
Before we create the subscribe in our Observable, we insert a \u201cone second\u201d waiting time, starting from the last text input, so that the call to services would not be run any time you wrote a new letter in the textarea. This is possible thanks to the operator debounce()<\/strong> insert in pipe<\/strong> to the Observable.<\/p>\n\n\n
\nthis.testoIstantaneo.pipe( debounceTime(1000) ).subscribe(testo => {\n\u00a0\n}\n<\/pre><\/div>\n\n\n
At this point, we are ready to run the calls to REST services in the subscribe. We request 4 translations: (from Italian to English, to Japanese, to Arabian, to Finnish), by the configuration of below objects:<\/p>\n\n\n
\nprivate impostaTraduzione() {\n\u00a0\u00a0\u00a0\u00a0this.impostazioniTraduzione = [\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0{ q: '', source: 'it', target: 'en', format: 'text' },\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0{ q: '', source: 'it', target: 'ja', format: 'text' },\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0{ q: '', source: 'it', target: 'ar', format: 'text' },\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0{ q: '', source: 'it', target: 'fi', format: 'text' }\n\u00a0\u00a0\u00a0\u00a0];\n\u00a0\u00a0}\n<\/pre><\/div>\n\n\n
These are the calls<\/p>\n\n\n
\nthis.testoIstantaneo.pipe( debounceTime(1000) ).subscribe(testo => {\n\u00a0\u00a0\u00a0\u00a0\u00a0this.azureSentiment.analize('it', testo).subscribe((sentiments: AzureSentimentResponse) => {\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0this.sentiments = sentiments;\n\u00a0\u00a0\u00a0\u00a0\u00a0});\n\u00a0\u00a0\u00a0\u00a0\u00a0this.traduzioni = [];\n\u00a0\u00a0\u00a0\u00a0\u00a0this.impostazioniTraduzione.forEach(impostazione => {\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0impostazione.q = testo;\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0this.googleTanslatorService.translate(impostazione).subscribe((response: GoogleTranslateResponse) => {\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0response.language = impostazione.target;\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0this.traduzioni.push(response);\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0});\n\u00a0\u00a0\u00a0\u00a0\u00a0});\n\u00a0\u00a0\u00a0});\n<\/pre><\/div>\n\n\n
The subscription makes available to the html template a translation array and an array including the sentiment analysis results.<\/p>\n\n\n
\n<div class="form-group">\n\u00a0\u00a0<label for="testoOriginale">Testo da Tradurre<\/label>\n\u00a0\u00a0<textarea class="form-control" id="testoOriginale" rows="3" [formControl]='testoOriginale'><\/textarea>\n<\/div>\n\u00a0\n<div *ngIf="sentiments && sentiments.documents">\n\u00a0\u00a0<div *ngFor="let sentiment of sentiments.documents">\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<div class="progress" style="margin-bottom: 10px">\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<div class="progress-bar progress-bar-striped progress-bar-animated"\u00a0 [ngClass]="{\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0'bg-success':sentiment.score >= 0.50,\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0'bg-danger': sentiment.score < 0.50}"\u00a0 role="progressbar" [attr.aria-valuenow]="sentiment.score*100" aria-valuemin="0" aria-valuemax="100"\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0[style.width.%]="sentiment.score*100"><\/div>\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0<\/div>\n\u00a0\u00a0<\/div>\n<\/div>\n\u00a0\n<div *ngFor="let traduzione of traduzioni">\n\u00a0\u00a0<app-translation [traduzione]='traduzione'><\/app-translation>\n<\/div>\n<\/pre><\/div>\n\n\n
The component app-translation is a bootstrap card, which displays the translation result.<\/p>\n\n\n
\n<div class="card text-white bg-primary mb-3">\n\u00a0\u00a0<div class="card-header">{{traduzione.language}}<\/div>\n\u00a0\u00a0<div class="card-body">\n\u00a0\u00a0\u00a0\u00a0<p *ngIf="traduzione && traduzione.data && traduzione.data.translations && traduzione.data.translations.length >0"\n\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0class="card-text">{{traduzione.data.translations[0].translatedText}}<\/p>\n\u00a0\u00a0<\/div>\n<\/div>\n<\/pre><\/div>\n\n\n
The progress bar will be green if the text analysis result is bigger or equal to 0,50 (that\u2019s to say if it expresses a positive mood), but it becomes red for lower values.<\/p>\n\n\n\n
\"\"<\/figure>\n\n\n\n
\"\"<\/figure>\n\n\n\n
You can find all the code here:<\/p>\n\n\n\n
\nhttps:\/\/github.com\/sorrentmutie\/lost-translations<\/a>\n<\/div><\/figure>\n\n\n\n
See you next!!<\/p>\n\n\n\n\n","protected":false},"excerpt":{"rendered":"
How to use Azure and Google in Cloud services to translate and the Sentiment Analysis from an Angular Client<\/p>\n","protected":false},"author":196716245,"featured_media":28685,"comment_status":"closed","ping_status":"closed","sticky":false,"template":"","format":"standard","meta":{"_et_pb_use_builder":"off","_et_pb_old_content":"","_et_gb_content_width":"","_coblocks_attr":"","_coblocks_dimensions":"","_coblocks_responsive_height":"","_coblocks_accordion_ie_support":"","_crdt_document":"","inline_featured_image":false,"_jetpack_newsletter_access":"","_jetpack_dont_email_post_to_subs":false,"_jetpack_newsletter_tier_id":0,"_jetpack_memberships_contains_paywalled_content":false,"_jetpack_memberships_contains_paid_content":false,"footnotes":"","jetpack_publicize_message":"","jetpack_publicize_feature_enabled":true,"jetpack_social_post_already_shared":true,"jetpack_social_options":{"image_generator_settings":{"template":"highway","default_image_id":0,"font":"","enabled":false},"version":2},"_wpas_customize_per_network":false,"jetpack_post_was_ever_published":false},"categories":[688637524],"tags":[688637390,688637381],"class_list":["post-28698","post","type-post","status-publish","format-standard","has-post-thumbnail","hentry","category-blog-en","tag-angular-en","tag-azure-en"],"yoast_head":"\nLost in Translations: translation and Sentiment Analysis in real time - 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