Harry Potter Character Interaction Networks: Analyzing character relationships, evolution, and narrative dynamics in J.K. Rowling's wizarding world using network science techniques and data from the Harry Potter series. Explore the magic of storytelling through data.

Statistical analysis investigating the relationship between information revelation patterns (measured via Kullback-Leibler divergence) and book popularity in English fiction. Features OLS and LASSO regression analysis, genre-specific modeling, and Project Gutenberg metadata analysis.

A Python implementation of hybrid Dynamic Topic Models and Large Language Models for detecting narrative shifts in longitudinal text corpora. Enables scalable analysis of media discourse evolution with statistical rigor and LLM-powered semantic interpretation.