1. Sentiment Analysis of GitHub Issues for Developer Insights


    To perform sentiment analysis on GitHub issues for developer insights, you would need to integrate several services. First, let's focus on obtaining the issues from a GitHub repository using the pulumi_github package, which provides a way to interact with GitHub resources.

    For sentiment analysis, typically, you would use a Natural Language Processing (NLP) service offered by many cloud providers like AWS Comprehend, Google Cloud Natural Language, or Azure Text Analytics. However, Pulumi does not directly provide an abstraction for performing sentiment analysis as it's not a resource that needs provisioning but rather a service you interact with dynamically.

    Here's what you can do though:

    1. Use Pulumi to provision a GitHub webhook that will trigger a cloud function or a serverless lambda function when a new issue is created or updated in your repository.
    2. Use the cloud function to call the NLP service of your choice, perform the sentiment analysis on the issue text, and then store the sentiment score in a database or a different system for insights aggregation.

    Below, I'll demonstrate how to set up a GitHub repository and a webhook using Pulumi with Python. Please note that for the sentiment analysis part, you would need to write the function's code to interact with the NLP service and deploy it separately.

    Let's set up the GitHub repository and a webhook:

    import pulumi import pulumi_github as github # Create a new GitHub repository repo = github.Repository("example-repo", description="Repository to demonstrate GitHub issues for sentiment analysis", visibility="public" ) # Create a webhook for the repository # The `webhook` will trigger on issues events and call a predefined URL (your cloud function endpoint) webhook = github.RepositoryWebhook("example-webhook", repository=repo.name, events=["issues"], configuration=github.RepositoryWebhookConfigurationArgs( url="https://your-cloud-function-endpoint.com/hook", # Replace with your actual cloud function's URL content_type="json" ) ) # Export the repository URL pulumi.export("repository_url", repo.html_url) # Export the webhook URL, useful for debugging or updating webhook settings pulumi.export("webhook_url", webhook.url)

    In the above program:

    • We create a new public GitHub repository using the Repository class from the pulumi_github package.
    • We then create a webhook that listens to issues events with the RepositoryWebhook class and tell it to call a cloud function endpoint (this URL should be replaced with your actual cloud function's URL).
    • Finally, we export the repository and webhook URLs so you can access them easily after deployment.

    For this code to work, you need to have Pulumi installed and configured to access your GitHub account with a personal access token. You also need to have a properly configured cloud function with an HTTP endpoint that can receive GitHub webhook calls and perform sentiment analysis.

    To perform sentiment analysis, you would write a function that is deployed to a cloud service, such as AWS Lambda, Google Cloud Functions, or Azure Functions. This function would use an NLP service API to process the issue text and determine the sentiment score.

    Please let me know if you want a more detailed guide or if you have any other Pulumi-related questions!