Analyzing Hacker News Comments with AI Clustering and Insights

This n8n workflow automates the process of extracting, clustering, and analyzing comments from a Hacker News story to generate community insights. It begins with a manual trigger and retrieves comments for a specified story using the Hacker News API. Before processing new data, it clears existing comments in a Qdrant vector store. The comments are then flattened and stored as vectors in the Qdrant database. Using advanced clustering algorithms like KMeans in Python, the workflow groups similar comments into clusters, which are filtered to include only those with at least three points. These clusters are fetched and analyzed with an OpenAI language model to generate summaries and insights about the community’s opinions. The insights are then exported to a Google Sheets document for easy review. This workflow is ideal for community managers, researchers, or businesses seeking to understand public sentiment and recurring themes in large comment datasets for storytelling, market research, or content moderation.