I Built a Literary Analysis Dashboard with AI — Here's What I Learned

What if you could upload a novel and get a full editorial-grade analysis — character maps, engagement curves, structural weaknesses — in minutes? I built Story Miner to do exactly that. It's a web app that combines deterministic text analysis with LLM-powered narrative intelligence to give writers and editors deep insight into their stories. Here's a walkthrough of what it does and how it's built. The Verdict — Strengths & Weaknesses at a Glance The first thing you see is a high-level report: what works, what doesn't, and actionable takeaways for the author. This is generated by feeding episode-level summaries through an LLM pipeline that identifies narrative patterns, then synthesizes them into a structured report. Engagement Map — Where Readers Stay (and Where They Leave) This tab maps narrative structure (Setup → Development → Crisis → Climax → Resolution) alongside an engagement timeline that tracks sentiment, dialogue ratio, and pacing across every episode. The engagement scores u