In this codelab you will focus on using the Natural Language API with Ruby. You will learn how to perform sentiment, entity and syntax analysis using Ruby!

The Google Cloud Natural Language API provides natural language understanding technologies to developers, including sentiment analysis, entity analysis, and syntax analysis.

What you'll learn

What you'll need

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Self-paced environment setup

If you don't already have a Google Account (Gmail or Google Apps), you must create one. Sign-in to Google Cloud Platform console (console.cloud.google.com) and create a new project:

Remember the project ID, a unique name across all Google Cloud projects (the name above has already been taken and will not work for you, sorry!). It will be referred to later in this codelab as PROJECT_ID.

Next, you'll need to enable billing in the Developers Console in order to use Google Cloud resources.

Running through this codelab shouldn't cost you more than a few dollars, but it could be more if you decide to use more resources or if you leave them running (see "cleanup" section at the end of this document). Google Container Engine pricing is documented here.

New users of Google Cloud Platform are eligible for a $300 free trial.

Start Cloud Shell

While Google Cloud can be operated remotely from your laptop, in this codelab you will be using Google Cloud Shell, a command line environment running in the Cloud. This Debian-based virtual machine is loaded with all the development tools you'll need (gcloud, ruby, gem and more), it offers a persistent 5GB home directory, and runs on the Google Cloud, greatly enhancing network performance and authentication. This means that all you will need for this codelab is a browser (yes, it works on a Chromebook).

Activate Google Cloud Shell

From the GCP Console click the icon (as depicted below) on the top right toolbar:

Then click "Start Cloud Shell" as shown here:

It should only take a few moments to provision and connect to the environment:

This virtual machine is loaded with all the development tools you'll need. It offers a persistent 5GB home directory, and runs on the Google Cloud, greatly enhancing network performance and authentication. Much if not all of your work in this lab can be done with simply a browser or your Google Chromebook.

Once connected to the cloud shell, you should see that you are already authenticated and that the project is already set to your PROJECT_ID:

gcloud auth list

Command output

Credentialed accounts:
 - <myaccount>@<mydomain>.com (active)
gcloud config list project

Command output

[core]
project = <PROJECT_ID>

If it is not, you can set it with this command:

gcloud config set project <PROJECT_ID>

Command output

Updated property [core/project].

Before you can begin using the Natural Language API you must enable the API. Using the Cloud Shell you can enable the API by using the following command:

gcloud service-management enable language.googleapis.com

In order to make requests to the Natural Language API, you need to use a Service Account. A Service Account is an account, belonging to your project, that is used by the Google Client Ruby library to make Natural Language API requests. Like any other user account, a service account is represented by an email address. In this section, you will use the gcloud tool to create a service account and then create credentials you will need to authenticate as the service account.

First you will set an environment variable with your PROJECT_ID which you will use throughout this codelab:

export GOOGLE_CLOUD_PROJECT="<PROJECT_ID>"

Next, you will create a new service account to access the Natural Language API by using:

gcloud iam service-accounts create my-nl-sa \
  --display-name "my nl codelab service account"

Next, you will create credentials that your Ruby code will use to log in as your new service account. Create these credentials and save it as a JSON file "~/key.json" by using the following command:

gcloud iam service-accounts keys create ~/key.json \
  --iam-account my-nl-sa@${GOOGLE_CLOUD_PROJECT}.iam.gserviceaccount.com

Finally, set the GOOGLE_APPLICATION_CREDENTIALS environment variable, which is used by the Natural Language API Ruby gem, covered in the next step, to find your credentials. The environment variable should be set to the full path of the credentials JSON file you created, by using:

export GOOGLE_APPLICATION_CREDENTIALS="/home/${USER}/key.json"

You can read more about authenticating the Natural Language API.

You can use the command line to install the Google Cloud Natural Language Ruby gem.

gem install google-cloud-language

You can read more about the set of Google Cloud service Ruby gems available for different APIs here.

Now that you have installed the required gem, start the Interactive Ruby tool by using irb.

irb

IRB will run the Ruby interpreter in a Read, Eval, Print, Loop session.

In this section you will perform Sentiment Analysis on a string and find out the Score and Magnitude using the Natural Language API.

The Score of the sentiment ranges between -1.0 (negative) and 1.0 (positive) and corresponds to the overall sentiment from the given information.

The Magnitude of the sentiment ranges from 0.0 to +inf and indicates the overall strength of sentiment from the given information. The more information that is provided the higher the magnitude.

Copy the following Ruby code into your IRB session:

require "google/cloud/language"

language = Google::Cloud::Language.new

document = language.document "Matz is great!"
sentiment = document.sentiment

puts "Score #{sentiment.score}, Magnitude #{sentiment.magnitude}"

This snippet of code will perform sentiment analysis on the string "Matz is great!". The score and magnitude will be printed similar to:

Score 0.699999988079071, Magnitude 0.699999988079071

Summary

In this step, you were able to perform Sentiment Analysis on a string of text and print out the score and magnitude. Read more about Sentiment Analysis.

Entity analysis inspects the given information for entities by searching for proper nouns such as public figures, landmarks, etc and returns information about those entities.

To perform Entity analysis, copy the following Ruby code into your IRB session.

require "google/cloud/language"

language = Google::Cloud::Language.new

document = language.document "Matz is great!"
entities = document.entities

entities.each do |entity|
  puts "Entity: #{entity.name} - #{entity.type}"
end

In this example you performed entity analysis on the string "Matz is great!". You should have seen the following output:

Entity: Matz - PERSON

Summary

In this step, you were able to perform Entity Analysis on a string of text and printed its entities. Read more about Entity Analysis.

Syntactic Analysis extracts linguistic information, breaking up the given text into a series of sentences and tokens (generally, word boundaries), providing further analysis on those tokens.

This example will print out the number of sentences, tokens, and provide the part of speech for each token.

To perform Syntax Analysis, copy the following Ruby code into your IRB session.

require "google/cloud/language"

language = Google::Cloud::Language.new
document = language.document "Matz is great!"
syntax   = document.syntax

puts "Sentences: #{syntax.sentences.count}"
puts "Tokens: #{syntax.tokens.count}"

syntax.tokens.each do |token|
  puts "#{token.part_of_speech.tag} #{token.text_span.text}"
end

In this example you performed syntax analysis on the string "Matz is great!". You should have seen the following output:

Sentences: 1
Tokens: 4
NOUN Matz
VERB is
ADJ great
PUNCT !

A visual interpretation is shown below.

Summary

In this step, you were able to perform Syntax Analysis on a simple string of text and printed out the number of sentences, number of tokens, and linguistic information for each token. Read more about Syntax Analysis.

You learned how to use the Natural Language API using Ruby to perform different kinds of analyses on information!

Clean up

To avoid incurring charges to your Google Cloud Platform account for the resources used in this quickstart:

Learn More

License

This work is licensed under a Creative Commons Attribution 2.0 Generic License.