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.
Update your virtual number 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.