System That Maps Keyword Clusters from Content

As creators and analysts, we often encounter an overwhelming stream of content—but without structure, it’s just noise. Over the past few days, I’ve been immersed in a journey to build something deceptively simple yet immensely powerful: a system that takes a blog homepage and transforms it into an interactive visual map of meaning.

Think of it as reverse-engineering the thought structure behind a blog.

🎯 The Goal

To go from a single blog URL to a dynamic cluster visualization of keywords, streamed and enriched with semantic understanding—in near real time.

🧩 Why This Problem Is Interesting

• Blogs are rich in insights, but underutilized unless indexed, clustered, and compared.
• Keyword lists are not enough—we need to understand themes, relationships, and semantic neighborhoods.
• Visualization is the key to explorability—not just knowing what’s said, but how concepts are connected.

This problem sits at the intersection of:

• Natural Language Processing (NLP)
• Async web scraping
• Embeddings and semantic clustering
• Real-time visual UX

🏗️ System Architecture

Here’s how the system works from end to end:

1. The user submits a blog homepage URL.
2. A crawler parses robots.txt, extracts links from sitemap.xml, and filters out non-blog links.
3. An async fetcher loads articles in parallel.
4. Text is cleaned using BeautifulSoup.
5. Keywords are extracted using KeyBERT or transformer-based models.
6. Each keyword is vectorized using sentence embeddings.
7. Keywords are clustered using K-Means and visualized with t-SNE.
8. The UI updates dynamically, streaming new clusters as data is processed.