GSP038

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, you learn how to use the Natural Language API to analyze entities, sentiment, and syntax.

Objectives

In this lab, you will learn how to:

• Create a Natural Language API request and calling the API with curl
• Extract entities and running sentiment analysis on text with the Natural Language API
• Perform linguistic analysis on text with the Natural Language API
• Create a Natural Language API request in a different language

Setup and requirements

Before you click the Start Lab button

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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:

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

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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.

Task 1. Create an API key

Since you use curl to send a request to the Natural Language API, you must generate an API key to pass in your request URL.

1. To create an API key, in the Cloud Console, select Navigation menu > APIs & Services > Credentials.

2. Click Create credentials and select API key.

3. Copy the generated API key and click Close.

Click Check my progress to verify the objective.

Create an API Key

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

1. Click Navigation menu > Compute Engine. You should see the provisioned linux instance, linux-instance, in the VM instances list.

2. Click on the SSH button. You will be 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 2. Make an entity analysis request

The first Natural Language API method you use is analyzeEntities. With this method, the API can extract entities (like people, places, and events) from text. To try it out the API's entity analysis, use the following sentence:

Joanne Rowling, who writes under the pen names J. K. Rowling and Robert Galbraith, is a British novelist and screenwriter who wrote the Harry Potter fantasy series.

You build your request to the Natural Language API in the file, request.json.

1. Use nano (a code editor) to create the file request.json:

nano request.json

2. Type or paste the following code into request.json:

{ "document":{ "type":"PLAIN_TEXT", "content":"Joanne Rowling, who writes under the pen names J. K. Rowling and Robert Galbraith, is a British novelist and screenwriter who wrote the Harry Potter fantasy series." }, "encodingType":"UTF8" }

3. Press CTRL+X to exit nano, then Y to save the file, then ENTER to confirm.

In the request, you're telling the Natural Language API about the text being sent. Supported type values are PLAIN_TEXT or HTML. In content, you pass the text to send to the Natural Language API for analysis.

The Natural Language API also supports sending files stored in Cloud Storage for text processing. If you wanted to send a file from Cloud Storage, you would replace content with gcsContentUri and give it a value of the text file's uri in Cloud Storage.

encodingType tells the API which type of text encoding to use when processing our text. The API will use this to calculate where specific entities appear in our text.

Click Check my progress to verify the objective.

Make an Entity Analysis Request

Task 3. Call the Natural Language API

1. You can now pass your request body, along with the API key environment variable you saved earlier, to the Natural Language API with the following curl command (all in one single command line):

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

2. In order to check the response run:

cat result.json

The beginning of your response should look like this:

"content": "Joanne Rowling", "beginOffset": 0 }, "type": "PROPER" }, { "text": { "content": "Rowling", "beginOffset": 53 }, "type": "PROPER" }, { "text": { "content": "novelist", "beginOffset": 96 }, "type": "COMMON" }, { "text": { "content": "Robert Galbraith", "beginOffset": 65 }, "type": "PROPER" } ] }, ... ] }

] }

For each entity in the response, you get the entity type, the associated Wikipedia URL if there is one, the salience, and the indices of where this entity appeared in the text. Salience is a number in the [0,1] range that refers to the centrality of the entity to the text as a whole.

The Natural Language API can also recognize the same entity mentioned in different ways. Take a look at the mentions list in the response: ​the API is able to tell that "Joanne Rowling", "Rowling", "novelist" and "Robert Galbriath" all point to the same thing.​

Click Check my progress to verify the objective.

Check the Entity Analysis response

Task 4. Sentiment analysis with the Natural Language API

In addition to extracting entities, the Natural Language API also lets you perform sentiment analysis on a block of text. This JSON request will include the same parameters as the request above, but this time change the text to include something with a stronger sentiment.

1. Use nano to replace the code in request.json with the following, and feel free to replace the content below with your own text:

{ "document":{ "type":"PLAIN_TEXT", "content":"Harry Potter is the best book. I think everyone should read it." }, "encodingType": "UTF8" }

2. Press CTRL+X to exit nano, then Y to save the file, then ENTER to confirm.

3. Next you send the request to the API's analyzeSentiment endpoint:

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

Your response should look like this:

{ "documentSentiment": { "magnitude": 1.9, "score": 0.9 }, "language": "en", "sentences": [ { "text": { "content": "Harry Potter is the best book.", "beginOffset": 0 }, "sentiment": { "magnitude": 0.9, "score": 0.9 } }, { "text": { "content": "I think everyone should read it.", "beginOffset": 31 }, "sentiment": { "magnitude": 0.9, "score": 0.9 } } ] } Note: Don't be alarmed if your scores differ slightly than the example output.

Notice that you get two types of sentiment values: sentiment for the document as a whole, and sentiment broken down by sentence. The sentiment method returns two values:

• score - is a number from -1.0 to 1.0 indicating how positive or negative the statement is.
• magnitude - is a number ranging from 0 to infinity that represents the weight of sentiment expressed in the statement, regardless of being positive or negative.

Longer blocks of text with heavily weighted statements have higher magnitude values. The score for the first sentence is positive (0.7), whereas the score for the second sentence is neutral (0.1).

Task 5. Analyzing entity sentiment

In addition to providing sentiment details on the entire text document, the Natural Language API can also break down sentiment by the entities in the text. Use this sentence as an example:

I liked the sushi but the service was terrible.

In this case, getting a sentiment score for the entire sentence as you did above might not be so useful. If this was a restaurant review and there were hundreds of reviews for the same restaurant, you'd want to know exactly which things people liked and didn't like in their reviews. Fortunately, the Natural Language API has a method that lets you get the sentiment for each entity in the text, called analyzeEntitySentiment. Let's see how it works!

1. Use nano to update request.json with the sentence below:

{ "document":{ "type":"PLAIN_TEXT", "content":"I liked the sushi but the service was terrible." }, "encodingType": "UTF8" }

2. Press CTRL+X to exit nano, then Y to save the file, then ENTER to confirm.

3. Then call the analyzeEntitySentiment endpoint with the following curl command:

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

In the response you get back two entity objects: one for "sushi" and one for "service". Here's the full JSON response:

{ "entities": [ { "name": "sushi", "type": "CONSUMER_GOOD", "metadata": {}, "salience": 0.51064336, "mentions": [ { "text": { "content": "sushi", "beginOffset": 12 }, "type": "COMMON", "sentiment": { "magnitude": 0, "score": 0 } } ], "sentiment": { "magnitude": 0, "score": 0 } }, { "name": "service", "type": "OTHER", "metadata": {}, "salience": 0.48935664, "mentions": [ { "text": { "content": "service", "beginOffset": 26 }, "type": "COMMON", "sentiment": { "magnitude": 0.7, "score": -0.7 } } ], "sentiment": { "magnitude": 0.7, "score": -0.7 } } ], "language": "en" }

You can see that the score returned for "sushi" was a neutral score of 0, whereas "service" got a score of -0.7. Cool! You also may notice that there are two sentiment objects returned for each entity. If either of these terms were mentioned more than once, the API would return a different sentiment score and magnitude for each mention, along with an aggregate sentiment for the entity.

Note: Don't be alarmed if your scores differ slightly than the example output.

Task 6. Analyzing syntax and parts of speech

Use syntactic analysis, another of the Natural Language API's methods, to dive deeper into the linguistic details of the text. analyzeSyntax extracts linguistic information, breaking up the given text into a series of sentences and tokens (generally, word boundaries), to provide further analysis on those tokens. For each word in the text, the API tells you the word's part of speech (noun, verb, adjective, etc.) and how it relates to other words in the sentence (Is it the root verb? A modifier?).

Try it out with a simple sentence. This JSON request will be similar to the ones above, with the addition of a features key. This tells the API to perform syntax annotation.

1. Use nano to replace the code in request.json with the following:

{ "document":{ "type":"PLAIN_TEXT", "content": "Joanne Rowling is a British novelist, screenwriter and film producer." }, "encodingType": "UTF8" }

2. Press CTRL+X to exit nano, then Y to save the file, then ENTER to confirm.

3. Then call the API's analyzeSyntax method:

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

The response should return an object like the one below for each token in the sentence:

{ "text": { "content": "is", "beginOffset": 15 }, "partOfSpeech": { "tag": "VERB", "aspect": "ASPECT_UNKNOWN", "case": "CASE_UNKNOWN", "form": "FORM_UNKNOWN", "gender": "GENDER_UNKNOWN", "mood": "INDICATIVE", "number": "SINGULAR", "person": "THIRD", "proper": "PROPER_UNKNOWN", "reciprocity": "RECIPROCITY_UNKNOWN", "tense": "PRESENT", "voice": "VOICE_UNKNOWN" }, "dependencyEdge": { "headTokenIndex": 2, "label": "ROOT" }, "lemma": "be" },

Let's break down the response:

• partOfSpeech tells you that "Joanne" is a noun.
• dependencyEdge includes data that you can use to create a dependency parse tree of the text. Essentially, this is a diagram showing how words in a sentence relate to each other. A dependency parse tree for the sentence above would look like this:

Note: You can create your own dependency parse trees in the browser with the Natural Language demo available in the Natural Language AI Guide

• headTokenIndex is the index of the token that has an arc pointing at "Joanne". Think of each token in the sentence as a word in an array.
• headTokenIndex of 1 for "Joanne" refers to the word "Rowling", which it is connected to in the tree. The label NN (short for noun compound modifier) describes the word's role in the sentence. "Joanne" modifies "Rowling", the subject of the sentence.
• lemma is the canonical form of the word. For example, the words run, runs, ran, and running all have a lemma of run. The lemma value is useful for tracking occurrences of a word in a large piece of text over time.

Task 7. Multilingual natural language processing

The Natural Language API also supports languages other than English (full list can be found in the Language Support Guide).

1. Modify the code in request.json with a sentence in Japanese:

{ "document":{ "type":"PLAIN_TEXT", "content":"日本のグーグルのオフィスは、東京の六本木ヒルズにあります" } }

2. Press CTRL+X to exit nano, then Y to save the file, then ENTER to confirm.

Notice that you didn't tell the API which language the text is, it can automatically detect it!

3. Next, you send it to the analyzeEntities endpoint:

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

And you get the following response:

{ "entities": [ { "name": "日本", "type": "LOCATION", "metadata": { "mid": "/m/03_3d", "wikipedia_url": "https://en.wikipedia.org/wiki/Japan" }, "salience": 0.23854347, "mentions": [ { "text": { "content": "日本", "beginOffset": 0 }, "type": "PROPER" } ] }, { "name": "グーグル", "type": "ORGANIZATION", "metadata": { "mid": "/m/045c7b", "wikipedia_url": "https://en.wikipedia.org/wiki/Google" }, "salience": 0.21155767, "mentions": [ { "text": { "content": "グーグル", "beginOffset": 9 }, "type": "PROPER" } ] }, ... ] "language": "ja" }

The wikipedia URLs even point to the Japanese Wikipedia pages - so cool!

Congratulations!

You've learned how to perform text analysis with the Cloud Natural Language API by extracting entities, analyzing sentiment, and doing syntax annotation. In this lab, you created a Natural Language API request and called the API with curl, extracted entities and ran sentiment analysis on text with the Natural Language API, performed linguistic analysis on text, and created a Natural Language API request in a different language.

Next steps

• Check out the Natural Language API tutorials in the documentation.

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Manual Last Updated February 14, 2024

Lab Last Tested October 13, 2023

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