Create an API Key
Create a request to Classify a news article
Check the Entity Analysis response
Create a new Dataset and table for categorized text data
Classify Text into Categories with the Natural Language API
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.
What you'll learn
Creating a Natural Language API request and calling the API with curl
Using the NL API's text classification feature
Using text classification to understand a dataset of news articles
What you'll need
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).
- Time to complete the lab---remember, once you start, you cannot pause a lab.
How to start your lab and sign in to the Google Cloud Console
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
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.
If necessary, copy the Username from the Lab Details panel and paste it into the Sign in dialog. Click Next.
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.
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.
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.
Click Activate Cloud Shell at the top of the Google Cloud console.
It takes a few moments to provision and connect to the environment. 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:
gcloud is the command-line tool for Google Cloud. It comes pre-installed on Cloud Shell and supports tab-completion.
(Optional) You can list the active account name with this command:
(Optional) You can list the project ID with this command:
gcloud, in Google Cloud, refer to the gcloud CLI overview guide.
Confirm that the Cloud Natural Language API is enabled
Click the Navigation menu icon in the top left of the screen.
Select APIs & Services > Enabled APIs and Services.
Click Enable APIs and Services.
Then, search for
language in the search box. Click Cloud Natural Language API:
If the API is not enabled, you'll see the Enable button. Click Enable to enable the Cloud Natural Language API:
When the API is enabled, Google Cloud displays API information as follows:
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.
To create an API key, in your Console, click Navigation menu > APIs & Services > Credentials:
Then click Create credentials:
In the drop down menu, select API key:
Next, copy the key you just generated. Then click Close.
Click Check my progress to verify the objective.
Now that you have an API key, you will 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. Open the Navigation menu and select Compute Engine > VM Instances. You should see the following provisioned linux instance:
Click on the SSH button. You will be brought to an interactive shell. In the command line, enter in the following, replacing
<YOUR_API_KEY> with the key you just copied:
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 here.
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. 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.
request.json file with the code below. You can create the file using one of your preferred command line editors (nano, vim, emacs).
Create a new file named
request.json and add the following:
Now you can send this text to the Natural Language API's
classifyText method with the following
Look at the response:
You created an Speech API request then called the Speech API. Run the following command to save the response in a
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.
Classifying 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):
Next you'll create a BigQuery table for your data.
Creating 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.
Navigate to Navigation menu > BigQuery in the Console.
To create a dataset, click on the View actions icon next to your project ID and select Create dataset:
Name the dataset news_classification_dataset, then click Create dataset.
To create a table, click on the View actions icon next to the news_classification_dataset and select Create Table.
Use the following settings for the new table:
- Create table from: Empty table
- Name your table: article_data
- Click Add Field and add the following 3 fields: article_text, category, and confidence.
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.
Classifying news data and storing the result in BigQuery
In order to perform next steps please connect to the Cloud Shell.
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.
Export the name of your Cloud project as an environment variable. Replace
<your_project_name> with the Project ID found in the CONNECTION DETAILS section of the lab:
Run the following commands to create a service account:
Now you're ready to send the text data to the Natural Language API!
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.
Create a file called
classify-text.py and copy the following into it. You can either create the file using one of your preferred command line editors (nano, vim, emacs). Replace
YOUR_PROJECT with your Project ID:
Now you're ready to start classifying articles and importing them to BigQuery.
Run the following script:
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
When the script has finished running, it's time to verify that the article data was saved to BigQuery.
In BigQuery, navigate to the
article_data table in the Explorer tab and click Query > In new tab to query the table:
Edit the results in the Unsaved query box, adding an asterisk between SELECT and FROM:
Now click Run.
You will see your data when the query completes. 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.
Analyzing categorized news data in BigQuery
First, see which categories were most common in the dataset.
In the BigQuery console, click + Compose New Query.
Enter the following query, replacing
YOUR_PROJECT with your project name:
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:
Or, you could get only the articles where the Natural language API returned a confidence score greater than 90%:
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 Data Studio quickstart for BigQuery.
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.
What was covered
- Creating a Natural Language API
classifyTextrequest and calling the API with
- Using the Google Cloud Python module to analyze a large news dataset
- Importing and analyzing data in BigQuery
Finish your quest
This self-paced lab is part of the Qwiklabs Machine Learning APIs, Data Engineering, and Language, Speech, Text, & Translation with Google Cloud APIs Quests. 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 a Quest and get immediate completion credit if you've taken this lab. See other available Qwiklabs Quests.
Take your next lab
Continue your Quest with Predict Visitor Purchases with a Classification Model in BQML or try one of these:
Next Steps / Learn More
- Check out the docs for classifying content with the Natural Language API.
- Learn more about BigQuery in the documentation.
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Manual Last Updated August 02, 2022
Lab Last Tested August 02, 2022
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