In this use case, we have API Gateway REST API as a proxy to Amazon Comprehend's DetectSentiment API operation. Learn more about this pattern at Serverless Land Patterns: https://serverlessland.com/patterns/apigw-comprehend.

Important: this application uses various AWS services and there are costs associated with these services after the Free Tier usage - please see the AWS Pricing page for details. You are responsible for any AWS costs incurred. No warranty is implied in this example.

• Create an AWS account if you do not already have one and log in. The IAM user that you use must have sufficient permissions to make necessary AWS service calls and manage AWS resources.
• AWS CLI installed and configured
• Git Installed
• AWS Serverless Application Model (AWS SAM) installed

1. Create a new directory, navigate to that directory in a terminal and clone the GitHub repository:

   git clone https://github.com/aws-samples/serverless-patterns
   

2. Change directory to the pattern directory:

   cd serverless-patterns/apigw-comprehend
   

3. From the command line, use AWS SAM to deploy the AWS resources for the pattern as specified in the template.yml file:

   sam deploy --guided
   

4. During the prompts:

   • Enter a stack name
   • Enter the desired AWS Region
   • Allow SAM CLI to create IAM roles with the required permissions.

   Once you have run sam deploy --guided mode once and saved arguments to a configuration file (samconfig.toml), you can use sam deploy in future to use these defaults.

5. Note the outputs from the SAM deployment process. These contain the resource names and/or ARNs which are used for testing.

To test the endpoint first send data using the following command. Be sure to update the API Endpoint from the Cloudformation Stack Output.

curl --location --request POST '[YOUR API ENDPOINT]' --header 'Content-Type: application/json' \
--data-raw '{
        "LanguageCode": "String",
        "Text": "String"
    }'

You will observe a complete sentiment analysis of the 'Text' inputs that are passed as payload to your API endpoint as follows:

1. Test payload 1 -

   "LanguageCode": "en",
   "Text": "Comprehend is great to use."
    }

Output:

{
    "Sentiment": "POSITIVE",
    "SentimentScore": {
        "Mixed": 3.2229068892775103E-5,
        "Negative": 2.691979352675844E-5,
        "Neutral": 1.229004265042022E-4,
        "Positive": 0.9998179078102112
    }
}

1. Test payload 2 -

   "LanguageCode": "en",
   "Text": "It is not good to skip using Comprehend."
    }

Output:

{
    "Sentiment": "NEGATIVE",
    "SentimentScore": {
        "Mixed": 0.03624984249472618,
        "Negative": 0.9486008286476135,
        "Neutral": 0.004839026369154453,
        "Positive": 0.010310239158570766
    }
}

1. Test payload 3 -

   "LanguageCode": "en",
   "Text": "It's normal to use Comprehend."
    }

Output:

{
    "Sentiment": "NEUTRAL",
    "SentimentScore": {
        "Mixed": 0.002466087695211172,
        "Negative": 0.0034168041311204433,
        "Neutral": 0.8172720074653625,
        "Positive": 0.17684519290924072
    }
}

You can also run the following commands on a terminal prompt to get the API Endpoint -

API Endpoint

aws cloudformation describe-stacks --stack-name [YOUR STACK NAME] --query "Stacks[0].Outputs[?OutputKey=='ComprehendApiEndpoint'].OutputValue" --output text

1. Delete the stack

   sam delete
   

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