GSP063

Overview

The Cloud Natural Language API lets you extract entities from text, perform sentiment and syntactic analysis, and classify text into categories. In this lab, the focus is on text classification. Using a database of 700+ categories, this API feature makes it easy to classify a large dataset of text.

Objectives

In this lab, you will learn how to:

• Create a Natural Language API request and calling the API with curl
• Use the Natural Language API's text classification feature
• Use text classification to understand a dataset of news articles

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 Cloud 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 Cloud console (or right-click and select Open Link in Incognito Window if you are running the Chrome browser).

   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 below and paste it into the Sign in dialog.

   {{{user_0.username | "Username"}}}

   You can also find the Username in the Lab Details panel.

4. Click Next.

5. Copy the Password below and paste it into the Welcome dialog.

   {{{user_0.password | "Password"}}}

   You can also find the Password in the Lab Details panel.

6. Click Next.

   Important: You must use the credentials the lab provides you. Do not use your Google Cloud account credentials. Note: Using your own Google Cloud account for this lab may incur extra charges.

7. 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 Google Cloud console opens in this tab.

Note: To view a menu with a list of Google Cloud products and services, click the Navigation menu at the top-left.

Activate Cloud Shell

Cloud Shell is a virtual machine that is loaded with development tools. It offers a persistent 5GB home directory and runs on the Google Cloud. Cloud Shell provides command-line access to your Google Cloud resources.

1. Click Activate Cloud Shell at the top of the Google Cloud console.

When you are connected, you are already authenticated, and the project is set to your Project_ID, . The output contains a line that declares the Project_ID for this session:

Your Cloud Platform project in this session is set to {{{project_0.project_id | "PROJECT_ID"}}}

gcloud is the command-line tool for Google Cloud. It comes pre-installed on Cloud Shell and supports tab-completion.

2. (Optional) You can list the active account name with this command:

gcloud auth list

3. Click Authorize.

Output:

ACTIVE: * ACCOUNT: {{{user_0.username | "ACCOUNT"}}} To set the active account, run: $ gcloud config set account `ACCOUNT`

4. (Optional) You can list the project ID with this command:

gcloud config list project

Output:

[core] project = {{{project_0.project_id | "PROJECT_ID"}}} Note: For full documentation of gcloud, in Google Cloud, refer to the gcloud CLI overview guide.

Task 1. Enable the Cloud Natural Language API

1. Expand the Navigation menu () at the top left of the screen.

2. Select APIs & Services > Enabled APIs and Services.

3. Click Enable APIs and Services.

4. Then, search for language in the search box.

5. Click Cloud Natural Language API:

If the API is not enabled, you'll see the Enable button.

6. Click Enable to enable the Cloud Natural Language API.

When the API is enabled, Google Cloud displays API information as follows:

Task 2. Create an API key

Since you're using curl to send a request to the Natural Language API, you need to generate an API key to pass in the request URL.

1. To create an API key, in your Console, click Navigation menu > APIs & Services > Credentials.

2. Then click Create Credentials.

3. In the drop down menu, select API key.

4. Next, copy the key you just generated, then click Close.

Click Check my progress to verify the objective.

Create an API Key

Now that you have an API key, you save it as an environment variable to avoid having to insert the value of your API key in each request.

In order to perform next steps please connect to the instance provisioned for you via ssh.

1. Open the Navigation menu and select Compute Engine > VM Instances. You should see a provisioned linux-instance.

2. Click on the SSH button. You are brought to an interactive shell.

3. In the command line, enter in the following, replacing <YOUR_API_KEY> with the key you just copied:

export API_KEY=<YOUR_API_KEY>

Task 3. Classify a news article

Using the Natural Language API's classifyText method, you can sort text data into categories with a single API call. This method returns a list of content categories that apply to a text document.

These categories range in specificity, from broad categories like /Computers & Electronics to highly specific categories such as /Computers & Electronics/Programming/Java (Programming Language). A full list of 700+ possible categories can be found in the Content Categories page.

You'll start by classifying a single article, and then see how you can use this method to make sense of a large news corpus.

1. To start, take this headline and description from a New York Times article in the food section:

A Smoky Lobster Salad With a Tapa Twist. This spin on the Spanish pulpo a la gallega skips the octopus, but keeps the sea salt, olive oil, pimentón and boiled potatoes.

2. Create a file named request.json and add the code found below. You can create the file using one of your preferred command line editors (nano, vim, emacs).

{ "document":{ "type":"PLAIN_TEXT", "content":"A Smoky Lobster Salad With a Tapa Twist. This spin on the Spanish pulpo a la gallega skips the octopus, but keeps the sea salt, olive oil, pimentón and boiled potatoes." } } Create a request to classify a news article

3. Now you can send this text to the Natural Language API's classifyText method with the following curl command:

curl "https://language.googleapis.com/v1/documents:classifyText?key=${API_KEY}" \ -s -X POST -H "Content-Type: application/json" --data-binary @request.json

Look at the response:

{ categories: [ { name: '/Food & Drink/Cooking & Recipes', confidence: 0.85 }, { name: '/Food & Drink/Food/Meat & Seafood', confidence: 0.63 } ] }

You created a Speech API request then called the Speech API.

4. Run the following command to save the response in a result.json file:

curl "https://language.googleapis.com/v1/documents:classifyText?key=${API_KEY}" \ -s -X POST -H "Content-Type: application/json" --data-binary @request.json > result.json Check the Entity Analysis response

The API returned 2 categories for this text:

• /Food & Drink/Cooking & Recipes
• /Food & Drink/Food/Meat & Seafood

The text doesn't explicitly mention that this is a recipe or even that it includes seafood, but the API is able to categorize it. Classifying a single article is cool, but to really see the power of this feature, classify lots of text data.

Task 4. Classify a large text dataset

To see how the classifyText method can help you understand a dataset with lots of text, use this public dataset of BBC news articles. The dataset consists of 2,225 articles in five topic areas (business, entertainment, politics, sports, tech) from 2004 - 2005. A subset of these articles are in a public Cloud Storage bucket. Each of the articles is in a .txt file.

To examine the data and send it to the Natural Language API, you'll write a Python script to read each text file from Cloud Storage, send it to the classifyText endpoint, and store the results in a BigQuery table. BigQuery is Google Cloud's big data warehouse tool - it lets you easily store and analyze large data sets.

• To see the type of text you'll be working with, run the following command to view one article (gsutil provides a command line interface for Cloud Storage):

gsutil cat gs://spls/gsp063/bbc_dataset/entertainment/001.txt

Next you'll create a BigQuery table for your data.

Task 5. Create a BigQuery table for categorized text data

Before sending the text to the Natural Language API, you need a place to store the text and category for each article.

1. Navigate to Navigation menu > BigQuery in the Console.

2. Click Done.

3. To create a dataset, click on the View actions icon next to your Project ID and select Create dataset:

4. Name the dataset news_classification_dataset, then click Create dataset.

5. To create a table, click on the View actions icon next to the news_classification_dataset and select Create Table.

6. Use the following settings for the new table:

   • Create table from: Empty table
   • Name your table: article_data

7. Under Schema, click Add Field and add the following 3 fields:

Field Name	Type	Mode
article_text	STRING	NULLABLE
category	STRING	NULLABLE
confidence	FLOAT	NULLABLE

8. Click Create Table.

The table is empty right now. In the next step you'll read the articles from Cloud Storage, send them to the Natural Language API for classification, and store the result in BigQuery.

Click Check my progress to verify the objective.

Create a new Dataset and table for categorized text data

Task 6. Classify news data and store the result in BigQuery

In order to perform next steps please connect to the Cloud Shell. If prompted click continue.

Before writing a script to send the news data to the Natural Language API, you need to create a service account. This will be used to authenticate to the Natural Language API and BigQuery from a Python script.

1. Export your Project ID as an environment variable:

export PROJECT={{{project_0.project_id | Project ID}}}

2. Run the following commands to create a service account:

gcloud iam service-accounts create my-account --display-name my-account gcloud projects add-iam-policy-binding $PROJECT --member=serviceAccount:my-account@$PROJECT.iam.gserviceaccount.com --role=roles/bigquery.admin gcloud projects add-iam-policy-binding $PROJECT --member=serviceAccount:my-account@$PROJECT.iam.gserviceaccount.com --role=roles/serviceusage.serviceUsageConsumer gcloud iam service-accounts keys create key.json --iam-account=my-account@$PROJECT.iam.gserviceaccount.com export GOOGLE_APPLICATION_CREDENTIALS=key.json

Now you're ready to send the text data to the Natural Language API!

3. Write a Python script using the Python module for Google Cloud.

You can accomplish the same thing from any language, there are many different cloud client libraries.

4. Create a file called classify-text.py and copy the following code into it. You can either create the file using one of your preferred command line editors (nano, vim, emacs).

from google.cloud import storage, language, bigquery # Set up your GCS, NL, and BigQuery clients storage_client = storage.Client() nl_client = language.LanguageServiceClient() bq_client = bigquery.Client(project='{{{project_0.project_id | Project ID}}}') dataset_ref = bq_client.dataset('news_classification_dataset') dataset = bigquery.Dataset(dataset_ref) table_ref = dataset.table('article_data') table = bq_client.get_table(table_ref) # Send article text to the NL API's classifyText method def classify_text(article): response = nl_client.classify_text( document=language.Document( content=article, type=language.Document.Type.PLAIN_TEXT ) ) return response rows_for_bq = [] files = storage_client.bucket('qwiklabs-test-bucket-gsp063').list_blobs() print("Got article files from GCS, sending them to the NL API (this will take ~2 minutes)...") # Send files to the NL API and save the result to send to BigQuery for file in files: if file.name.endswith('txt'): article_text = file.download_as_bytes().decode('utf-8') # Decode bytes to string nl_response = classify_text(article_text) if len(nl_response.categories) > 0: rows_for_bq.append((article_text, nl_response.categories[0].name, nl_response.categories[0].confidence)) print("Writing NL API article data to BigQuery...") # Write article text + category data to BQ if rows_for_bq: errors = bq_client.insert_rows(table, rows_for_bq) if errors: print("Encountered errors while writing to BigQuery:", errors) else: print("No articles found in the specified bucket.")

Now you're ready to start classifying articles and importing them to BigQuery.

5. Run the following script:

python3 classify-text.py

The script takes about two minutes to complete, so while it's running read about what's happening.

You're using the Google Cloud Python client library to access Cloud Storage, the Natural Language API, and BigQuery. First, a client is created for each service, then references are created to the BigQuery table. files is a reference to each of the BBC dataset files in the public bucket. The files are looked at, the articles are downloaded as strings, then each one is sent to the Natural Language API in the classify_text function. For all articles where the Natural Language API returns a category, the article and its category data are saved to a rows_for_bq list. When classifying each article is done, the data is inserted into BigQuery using insert_rows().

Note: The Natural Language API can return more than one category for a document, but for this lab you're only storing the first category returned to keep things simple.

When the script has finished running, it's time to verify that the article data was saved to BigQuery.

6. In BigQuery, navigate to the article_data table in the Explorer tab and click Query > In new tab to query the table:

7. Edit the results in the Unsaved query box, adding an asterisk between SELECT and FROM:

SELECT * FROM `{{{project_0.project_id | Project ID}}}.news_classification_dataset.article_data`

8. Now click Run.

You'll see your data when the query completes.

9. Scroll to the right to see the category column.

The category column has the name of the first category the Natural Language API returned for the article, and confidence is a value between 0 and 1 indicating how confident the API is that it categorized the article correctly.

You'll learn how to perform more complex queries on the data in the next step.

Task 7. Analyze categorized news data in BigQuery

First, see which categories were most common in the dataset.

1. In the BigQuery console, click + SQL query.

2. Enter the following query:

SELECT category, COUNT(*) c FROM `{{{project_0.project_id | Project ID}}}.news_classification_dataset.article_data` GROUP BY category ORDER BY c DESC

3. Now click Run.

You should see something like this in the query results:

If you wanted to find the article returned for a more obscure category like /Arts & Entertainment/Music & Audio/Classical Music, you could write the following query:

SELECT * FROM `{{{project_0.project_id | Project ID}}}.news_classification_dataset.article_data` WHERE category = "/Arts & Entertainment/Music & Audio/Classical Music"

Or, you could get only the articles where the Natural language API returned a confidence score greater than 90%:

SELECT article_text, category FROM `{{{project_0.project_id | Project ID}}}.news_classification_dataset.article_data` WHERE cast(confidence as float64) > 0.9

To perform more queries on your data, explore the BigQuery documentation. BigQuery also integrates with a number of visualization tools. To create visualizations of your categorized news data, check out the Looker Studio for BigQuery.

Congratulations!

You've learned how to use the Natural Language API text classification method to classify news articles. You started by classifying one article, and then learned how to classify and analyze a large news dataset using the NL API with BigQuery. You also learned how to create a BigQuery table and run queries on your data.

Next steps / learn more

• Read the docs for classifying content with the Natural Language API.
• Learn more about BigQuery in the documentation.

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Manual Last Updated December 05, 2024

Lab Last Tested December 05, 2024

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