Bestseller Narrative Analytics Platform

Mary Kurt

Completed work

Data Analyst

Data Scientist

Web Developer

Next.js

Python

Supabase

Publishing

📚 Bestseller‑Anatomy

A comprehensive narrative analytics platform that decodes the DNA of bestsellers through multi-dimensional analysis. By examining structural patterns, emotional arcs, and narrative techniques across successful novels, this project reveals the underlying mechanics that make stories resonate with readers. The project consists of two main components:

A proprietary analysis engine (closed source) that powers the metrics

An open-source web interface that provides:

Deep insights into what makes bestsellers successful through comprehensive analysis of successful fictions

Interactive metrics dashboard for analyzing your own manuscript

🚀 Live Demo

👉 Analysis showcase: https://v0-bestseller-website-design.vercel.app/

The demo has two main features:

Anatomy of a Bestseller – Explore detailed insights and patterns from our analysis of successful novels, revealing the key elements that make stories resonate with readers

Analyze My Book – Upload your manuscript (TXT) to receive a comprehensive metrics dashboard showing how your story compares to bestseller patterns

🔮 Coming Soon

Predictive Analytics Engine

Bestseller Probability Score: Get an AI-powered prediction of your manuscript's potential success

Success Factors Analysis: Understand which elements contribute to or detract from your book's bestseller potential

Comparative Market Analysis: See how your book compares to current market trends

Optimization Suggestions: Receive data-driven recommendations to improve your manuscript's market potential

How It Works

Upload your manuscript

Our AI analyzes your text against our bestseller database

Receive a comprehensive probability score

Get detailed insights into:

Market fit

Reader engagement potential

Competitive positioning

Optimization opportunities

🔍 Project Architecture

1) Analysis Engine (Proprietary)

The core analysis engine is built on extensive research and machine learning models. While the engine itself is not open source, here's what it does:

Research Foundation

Analyzed 80 contemporary bestsellers (≈ 8.3 M words)

Developed proprietary scoring algorithms

Implemented advanced NLP techniques

Technical Stack

Category Libraries/Tools Purpose Deep Learning & NLP PyTorch, Transformers BERT-based text analysis and inference Scientific Computing NumPy, SciPy Numerical operations and statistical analysis Data Processing Pandas Data manipulation and analysis Classical ML & Statistics scikit-learn, XGBoost Model preparation, metrics, and feature importance Feature Analysis SHAP (SHapley Additive exPlanations) Feature importance and model interpretation Dimensionality Reduction PCA, UMAP, t-SNE High-dimensional data visualization and pattern discovery Language Modeling SpaCy, Gensim, NLTK Tokenization, embeddings, word vectors Visualization Matplotlib, Plotly Statistical plots, interactive visualizations, and analysis dashboard System Tools os, pathlib, json, re, time File management, regex, debugging Environment Google Colab GPU CUDA acceleration for processing

Key Capabilities

Chapter-level sentiment analysis

Emotional arc tracking

Pacing and tension measurement

Style and readability metrics

Temporal and sensory analysis

Output Metrics

~100 raw metrics per chapter

~200 derived features

Statistical validation

Pattern recognition

Archetype identification

2) Web Interface (Open Source)

This repository contains the open-source web interface that connects to the analysis engine. It provides:

Features

TXT file upload

Real-time analysis processing

Interactive visualizations

Comprehensive dashboards

Export capabilities

Technical Implementation

Component Technology Purpose Frontend Next.js 14, React 19 User interface and interactions File Processing Supabase Storage Secure file handling Analysis Pipeline Edge Functions Serverless processing Data Storage Supabase Postgres Metrics persistence Visualization Plotly.js, Recharts Interactive charts

🛠️ Tech Stack

Framework: Next.js 15.2.4

Language: TypeScript

UI Library: React 19

Styling: Tailwind CSS

Component Library: Radix UI

Form Handling: React Hook Form with Zod validation

Data Visualization: Plotly.js, Recharts

Database: Supabase

State Management: React Hooks

Date Handling: date-fns

📦 Installation

Clone the repository:

git clone [repository-url]
cd bestseller_anatomy

Install dependencies:

pnpm install

Set up environment variables: Create a .env.local file in the root directory with the following variables:

NEXT_PUBLIC_SUPABASE_URL=your_supabase_url
NEXT_PUBLIC_SUPABASE_ANON_KEY=your_supabase_anon_key
SUPABASE_SERVICE_ROLE_KEY=your_service_role_key

🏃‍♂️ Development

Run the development server:

pnpm dev

The application will be available at http://localhost:3000

🏗️ Build

To create a production build:

pnpm build

To start the production server:

pnpm start

📁 Project Structure

bestseller_anatomy/
├── app/                    # Next.js app directory
│   ├── globals.css        # Global styles
│   ├── layout.tsx         # Root layout component
│   └── page.tsx           # Main page component
│
├── components/            # React components
│   ├── ui/               # Reusable UI components
│   ├── tabs/             # Tab-related components
│   ├── book-dashboard.tsx    # Book analysis dashboard
│   ├── chart-viewer.tsx      # Data visualization components
│   ├── nav-tabs.tsx          # Navigation components
│   ├── podcast-player.tsx    # Audio content player
│   └── table-of-contents.tsx # Content navigation
│
├── hooks/                # Custom React hooks
├── lib/                  # Utility functions and configurations
├── public/              # Static assets
├── styles/              # Global styles and Tailwind configurations
│
├── next.config.mjs      # Next.js configuration
├── tailwind.config.ts   # Tailwind CSS configuration
├── postcss.config.mjs   # PostCSS configuration
├── tsconfig.json        # TypeScript configuration
└── package.json         # Project dependencies and scripts

📊 Data Visualization

The web interface includes multiple visualization types for comprehensive analysis:

Distribution Analysis

Scatter/Bubble Plots: Readability vs. length distribution, cluster analysis

Box/Violin Plots: Chapter length distribution, word count variance

Progress Analysis

Line/Area Charts: Emotional flow, pacing variations, peak points across chapters

Timeline/Step Charts: Structural turning points (Disturbance, Doorways, Midpoint)

Progress Indicators: Chapter-by-chapter advancement

Comparative Analysis

Bar/Column Charts: Genre-based average reading times, feature comparisons

Stacked/100% Stacked Bars: Character traits, narrative POV, mood distributions

Donut/Pie Charts: Sensory balance (visual-auditory-tactile ratios)

Key Metrics

KPI Cards:

Readability scores

Word counts

Average chapter length

Key success metrics

Interactive Features

Zoom and pan capabilities

Tooltip information

Dynamic filtering

Export functionality

📝 License

GNU General Public License v3.0 (GPL-3.0)

This project is licensed under the GPL-3.0 License. Commercial use is not permitted.

👥 Contributing

Fork the repository

Create your feature branch (git checkout -b feature/amazing-feature)

Commit your changes (git commit -m 'Add some amazing feature')

Push to the branch (git push origin feature/amazing-feature)

Open a Pull Request

ℹ️ Note

This repository contains only the web interface code. The analysis engine that powers the metrics is proprietary and not included in this repository.

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Completed work

Posted May 21, 2025

The platform showcases insights from 2024's bestselling fiction and lets storytellers upload manuscripts for chapter-level analysis.

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Timeline

Apr 17, 2025 - May 20, 2025