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Graph-Based Text Classifier
Network analysis of Medium articles via graph theory, spanning trees, and centrality.
How it works
Takes three topics from the user, collects 15 Medium articles per topic through observed GraphQL requests, cleans and tokenizes the content, converts each article into a graph structure, and runs network analysis — then visualizes the result with Gravis.
- ›Accept three topics from the user as input.
- ›Use observed Medium GraphQL request patterns to fetch 15 articles per topic (45 total).
- ›Clean and tokenize text, removing stopwords, punctuation, and low-frequency terms.
- ›Build a graph per article where nodes are tokens and edges are co-occurrence within a window.
- ›Run network analysis: minimum spanning tree, degree centrality, betweenness centrality.
- ›Visualize graphs and spanning trees interactively with Gravis.
Engineering detail
Medium does not expose a simple public article-search API for this workflow. Understanding the request shape and constructing valid queries was a core part of the project.