This example uses Google Natural Language API to analyze SMS messages and determine the sentiment of the text.

SMS Messages sent through Nexmo will be sent to Google Natural Language API and a series of scores and tones returned to the console.

• Reference: https://cloud.google.com/natural-language/docs/sentiment-tutorial
• API Docs: https://cloud.google.com/natural-language/docs/reference/rest/v1beta2/documents/analyzeSentiment
• GitHub: https://github.com/googleapis/nodejs-language

Enable the Google Natural Language API and create a service user with the Project > Owner role. Download the google_creds.json file for the service user. More information can be found here -> https://cloud.google.com/natural-language/docs/reference/libraries

This sample app uses a .env file to provide the API key and URL.

Copy the provided .env.example file to a new file called .env:

cp .env.example > .env

Then update the values with the local file path of the google_creds.json file, and then save.

GOOGLE_APPLICATION_CREDENTIALS=

Also, expose the application to the internet using tools like ngrok. To see how, check out this guide.

To run the app using Docker, run the following command in a terminal:

docker-compose up

This will create a new image with all the dependencies and run it at http://localhost:3000.

To run the app using node, run the following command in a terminal:

npm install && node index.js

This will install all the dependencies and run it at http://localhost:3000.

For this example app a Nexmo number and SMS webhook setup is needed.

This can be achieved with the Nexmo CLI. Install the CLI by following these instructions.

Renting a number will need to be in place. This can also be achieved using the CLI by running this command:

nexmo number:buy --country_code US

Update the number created with the URL of the hosted or local server.

nexmo link:sms phone_number https://my-hostname/message

With the example Node application running in the terminal, send various SMS messages to the virtual number. The terminal will output the response from Google Natural Language API.

This app prints out to the console. For integration with an application, extend the analyzeTone function to suit your needs.