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Using the Natural Language API from Google Docs

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Using the Natural Language API from Google Docs

45 minutes 5 Credits

GSP126

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Overview

The Natural Language API is a pretrained machine learning model that can analyze syntax, extract entities, and evaluate the sentiment of text. It can be called from Google Docs to perform all of these functions.

This lab will walk you through calling the Natural Language API to recognize the sentiment of selected text in a Google Doc and highlight it based on that sentiment.

Once this lab is complete, you will be able to select text in a document and mark its sentiment, using a menu choice, as shown below.

4347c01adce8a7bd.png

Text will be highlighted in red for negative sentiment, green for positive sentiment, and yellow for neutral sentiment.

e87ab533eb3056d0.png

This lab is focused on calling the Natural Language API from Google Docs.

What you'll learn

  • How to call the Natural Language API from Google Docs

  • How to add menus to Google Docs

  • How to recognize and work with selected text in Google Docs

Setup and requirements

Before you click the Start Lab button

Read these instructions. Labs are timed and you cannot pause them. The timer, which starts when you click Start Lab, shows how long Google Cloud resources will be made available to you.

This hands-on lab lets you do the lab activities yourself in a real cloud environment, not in a simulation or demo environment. It does so by giving you new, temporary credentials that you use to sign in and access Google Cloud for the duration of the lab.

To complete this lab, you need:

  • Access to a standard internet browser (Chrome browser recommended).
Note: Use an Incognito or private browser window to run this lab. This prevents any conflicts between your personal account and the Student account, which may cause extra charges incurred to your personal account.
  • Time to complete the lab---remember, once you start, you cannot pause a lab.
Note: If you already have your own personal Google Cloud account or project, do not use it for this lab to avoid extra charges to your account.

How to start your lab and sign in to the Google Cloud Console

  1. Click the Start Lab button. If you need to pay for the lab, a pop-up opens for you to select your payment method. On the left is the Lab Details panel with the following:

    • The Open Google Console button
    • Time remaining
    • The temporary credentials that you must use for this lab
    • Other information, if needed, to step through this lab
  2. Click Open Google Console. The lab spins up resources, and then opens another tab that shows the Sign in page.

    Tip: Arrange the tabs in separate windows, side-by-side.

    Note: If you see the Choose an account dialog, click Use Another Account.
  3. If necessary, copy the Username from the Lab Details panel and paste it into the Sign in dialog. Click Next.

  4. Copy the Password from the Lab Details panel and paste it into the Welcome dialog. Click Next.

    Important: You must use the credentials from the left panel. Do not use your Google Cloud Skills Boost credentials. Note: Using your own Google Cloud account for this lab may incur extra charges.
  5. Click through the subsequent pages:

    • Accept the terms and conditions.
    • Do not add recovery options or two-factor authentication (because this is a temporary account).
    • Do not sign up for free trials.

After a few moments, the Cloud Console opens in this tab.

Note: You can view the menu with a list of Google Cloud Products and Services by clicking the Navigation menu at the top-left. Navigation menu icon

Task 1. Enable the Natural Language API

Before you start, make sure that the Natural Language API is enabled.

  1. In the Google Cloud console, select Navigation menu > APIs & Services > Library:

  2. Search for Cloud Natural Language API and click on the API to enable or to confirm that the API is enabled.

Task 2. Get an API Key

You need to generate an API user key to pass in the request URL.

  1. To create an API key, seclect Navigation menu > APIs & Services > Credentials:

  2. Click on Create credentials at the top and select API key:

  3. Copy the API key to a text file or a Google Doc to use in a later step. Click Close. Once you have the API key you are ready to move into Google Docs.

Click Check my progress to verify the objective. Get an API Key

Task 3. Set up your Google Doc

Before you can call the Natural Language API, you'll make an Apps Script program to create the menu, link it to a function to mark the text, and extract the text from the user selection.

  1. Create a new Google Doc.
  2. From within your new document, select the menu item Extensions > Apps Script.
  3. Delete any code in the script editor and paste in the code below. This code will create a menu item, extract the text from the current selected text, and highlight the text based on its sentiment. It does not call the Natural Language API yet.
/** * @OnlyCurrentDoc * * The above comment directs Apps Script to limit the scope of file * access for this add-on. It specifies that this add-on will only * attempt to read or modify the files in which the add-on is used, * and not all of the user's files. The authorization request message * presented to users will reflect this limited scope. */ /** * Creates a menu entry in the Google Docs UI when the document is * opened. * */ function onOpen() { var ui = DocumentApp.getUi(); ui.createMenu('Natural Language Tools') .addItem('Mark Sentiment', 'markSentiment') .addToUi(); } /** * Gets the user-selected text and highlights it based on sentiment * with green for positive sentiment, red for negative, and yellow * for neutral. * */ function markSentiment() { var POSITIVE_COLOR = '#00ff00'; // Colors for sentiments var NEGATIVE_COLOR = '#ff0000'; var NEUTRAL_COLOR = '#ffff00'; var NEGATIVE_CUTOFF = -0.2; // Thresholds for sentiments var POSITIVE_CUTOFF = 0.2; var selection = DocumentApp.getActiveDocument().getSelection(); if (selection) { var string = getSelectedText(); var sentiment = retrieveSentiment(string); // Select the appropriate color var color = NEUTRAL_COLOR; if (sentiment <= NEGATIVE_CUTOFF) { color = NEGATIVE_COLOR; } if (sentiment >= POSITIVE_CUTOFF) { color = POSITIVE_COLOR; } // Highlight the text var elements = selection.getSelectedElements(); for (var i = 0; i < elements.length; i++) { if (elements[i].isPartial()) { var element = elements[i].getElement().editAsText(); var startIndex = elements[i].getStartOffset(); var endIndex = elements[i].getEndOffsetInclusive(); element.setBackgroundColor(startIndex, endIndex, color); } else { var element = elements[i].getElement().editAsText(); foundText = elements[i].getElement().editAsText(); foundText.setBackgroundColor(color); } } } } /** * Returns a string with the contents of the selected text. * If no text is selected, returns an empty string. */ function getSelectedText() { var selection = DocumentApp.getActiveDocument().getSelection(); var string = ""; if (selection) { var elements = selection.getSelectedElements(); for (var i = 0; i < elements.length; i++) { if (elements[i].isPartial()) { var element = elements[i].getElement().asText(); var startIndex = elements[i].getStartOffset(); var endIndex = elements[i].getEndOffsetInclusive() + 1; var text = element.getText().substring(startIndex, endIndex); string = string + text; } else { var element = elements[i].getElement(); // Only translate elements that can be edited as text; skip // images and other non-text elements. if (element.editAsText) { string = string + element.asText().getText(); } } } } return string; } /** Given a string, will call the Natural Language API and retrieve * the sentiment of the string. The sentiment will be a real * number in the range -1 to 1, where -1 is highly negative * sentiment and 1 is highly positive. */ function retrieveSentiment (line) { // TODO: Call the Natural Language API with the line given // and return the sentiment value. return 0.0; } More details about Apps Script are available at the App Script site
  1. On the menu bar, click on the save project button save_button.png. (The script's name is shown to end users in several places, including the authorization dialog.)
  2. Return to your document. Add text to your document. You can use the sample that comes from Alice in Wonderland on Project Gutenberg (copy and paste the Plain Text UTF-8 version into the document), but feel free to use any text you wish.
  3. Reload the document to see the new menu, Natural Language Tools, which you created, appear in the Google Docs toolbar.
  4. Select text and then the Mark Sentiment option from the Natural Language Tools menu. The first time you select this option, you will be prompted to authorize the script to run. Click Authorize, and then log in with your credentials.
  5. Allow Natural Language Tools to view and manage documents that this application has been installed in.
  6. Once the script is authorized, the selected text will be highlighted in yellow, since the stub for sentiment analysis always returns 0.0, which is neutral.

sentiment_result.png

Click Check my progress to verify the objective. Set up your Google Doc

Task 4. Call the Natural Language API

Once your program can extract text from the selection and highlight it, it's time to call the Natural Language API. All of this will be done in the body of the retrieveSentiment function.

Find all the details of the Natural Language API here.
  1. Return to the Extensions > Apps Script in Google Docs.
  2. In the retrieveSentiment function, remove the current lines and add a variable to contain your API key, which you saved in the "Get an API key" section.
var apiKey = "your key here";
  1. Create a variable that will hold the URL of the Natural Language API with your API key appended to it.
var apiEndpoint = 'https://language.googleapis.com/v1/documents:analyzeSentiment?key=' + apiKey;
  1. Build a structure from the line passed into the function that holds the text of the line, along with its type and language. Currently the only supported language is English.
var docDetails = { language: 'en-us', type: 'PLAIN_TEXT', content: line };
  1. Build the entire data payload from the document details by adding the encoding type.
var nlData = { document: docDetails, encodingType: 'UTF8' };
  1. Create a structure containing the payload and the necessary header information.
var nlOptions = { method : 'post', contentType: 'application/json', payload : JSON.stringify(nlData) };
  1. Make the call, saving the response.
var response = UrlFetchApp.fetch(apiEndpoint, nlOptions);
  1. The response will be returned in JSON format, so parse it and extract the score field, if it exists. Return either that field or 0.0.
var data = JSON.parse(response); var sentiment = 0.0; // Ensure all pieces were in the returned value if (data && data.documentSentiment && data.documentSentiment.score){ sentiment = data.documentSentiment.score; } return sentiment;

The complete code to retrieve the sentiment is below.

function retrieveSentiment (line) { var apiKey = "your key here"; var apiEndpoint = 'https://language.googleapis.com/v1/documents:analyzeSentiment?key=' + apiKey; // Create a structure with the text, its language, its type, // and its encoding var docDetails = { language: 'en-us', type: 'PLAIN_TEXT', content: line }; var nlData = { document: docDetails, encodingType: 'UTF8' }; // Package all of the options and the data together for the call var nlOptions = { method : 'post', contentType: 'application/json', payload : JSON.stringify(nlData) }; // And make the call var response = UrlFetchApp.fetch(apiEndpoint, nlOptions); var data = JSON.parse(response); var sentiment = 0.0; // Ensure all pieces were in the returned value if (data && data.documentSentiment && data.documentSentiment.score){ sentiment = data.documentSentiment.score; } return sentiment; }
  1. Save your script, reload the document, and try out the full program. You may need to re-authorize with your credentials to enable the new functionality. Select different sections of your document to see how the sentiment may differ over its parts.

sentiment.png

Congratulations!

You've created a Google Doc and called the Natural Language API to analyze the sentiment of selected portions of the document.

Completion Badge

Finish your Quest

This self-paced lab is part of the Workspace Integrations Quest. A Quest is a series of related labs that form a learning path. Completing this Quest earns you the badge above, to recognize your achievement. You can make your badge (or badges) public and link to them in your online resume or social media account. Enroll in this Quest and get immediate completion credit if you've taken this lab. See other available Quests.

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Manual Last Updated September 30, 2022
Lab Last Tested September 30, 2022

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