
准备工作
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The Cloud Natural Language API lets you extract entities from text, perform sentiment and syntactic analysis, and classify text into categories. In this lab, we'll focus on text classification. Using a database of 700+ categories, this API feature makes it easy to classify a large dataset of text.
For each lab, you get a new Google Cloud project and set of resources for a fixed time at no cost.
Sign in to Qwiklabs using an incognito window.
Note the lab's access time (for example, 1:15:00
), and make sure you can finish within that time.
There is no pause feature. You can restart if needed, but you have to start at the beginning.
When ready, click Start lab.
Note your lab credentials (Username and Password). You will use them to sign in to the Google Cloud Console.
Click Open Google Console.
Click Use another account and copy/paste credentials for this lab into the prompts.
If you use other credentials, you'll receive errors or incur charges.
Accept the terms and skip the recovery resource page.
Google 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.
Google Cloud Shell provides command-line access to your Google Cloud resources.
In Cloud console, on the top right toolbar, click the Open Cloud Shell button.
Click Continue.
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. For example:
gcloud is the command-line tool for Google Cloud. It comes pre-installed on Cloud Shell and supports tab-completion.
Output:
Example output:
Output:
Example output:
Click the Navigation menu icon () at 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, you see the MANAGE button on the Cloud Natural Language API tile.
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.
Now that you have an API key, save it to an environment variable to avoid having to insert the value of your API key in each request.
<your_api_key>
with the key you just copied: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 Guide.
We'll start by classifying a single article, and then we'll see how we can use this method to make sense of a large news corpus. To start, let's 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 either create the file using one of your preferred command line editors (nano, vim, emacs) or use the Cloud Shell code editor:request.json
and add the following:classifyText
method with the following curl
command:Look at the response:
Output:
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, let's classify lots of text data.
To see how the classifyText
method can help us understand a dataset with lots of text, you'll 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 Google 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:
Next you'll create a BigQuery table for your data.
Before sending the text to the Natural Language API, you need a place to store the text and category for each article.
In the Google Cloud console, click Navigation menu () > BigQuery.
Click Done for the welcome notice when launching BigQuery.
In the left panel, click the View actions icon () next to your project name and click Create dataset.
For Dataset ID, type news_classification_dataset
Click Create dataset.
Click on the View actions icon next to your dataset name and click Create table. Use the following settings for the new 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.
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.
<your_project_name>
with the GCP Project ID found in the Lab details panel of the lab.Now you're ready to send the text data to the Natural Language API!
To do that, 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.
classify-text.py
and copy the following into it. Replace YOUR_PROJECT
with your GCP Project ID.Now you're ready to start classifying articles and importing them to BigQuery.
The script takes about two minutes to complete, so while it's running let's discuss what's happening.
python3 classify-text.py
, the cloud shell might be disconnected. In order to fix that, please export your environment variables by running the below commands then re-run the python3 classify-text.py
command.
export PROJECT= (GCP PROJECT ID)
export GOOGLE_APPLICATION_CREDENTIALS=key.json
We'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. We iterate through these files, download the articles as strings, and send each one to the Natural Language API in our 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()
.
When the script has finished running, it's time to verify that the article data was saved to BigQuery.
article_data
table in the BigQuery tab and click QUERY > In new tab: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.
First, see which categories were most common in the dataset.
In the BigQuery console, click + Compose new query.
Enter the following query:
You should see something like this in the query results:
/Arts & Entertainment/Music & Audio/Classical Music
, you could run the following query: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.
classifyText
request and calling the API with curl
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