Book File Pattern Analysis Report

I have a collection of books saved as Word and plain-text documents that I’d like mined for insight. The goal is simple: scan the entire set, detect repeated words and broader usage trends, and turn those findings into a clear, reader-friendly report.

You’re free to use the text-analysis toolkit you know best—Python (NLTK, spaCy), R (tidytext), even advanced Excel or Power BI—so long as the final output meets the acceptance criteria below.

Deliverables
• Frequency analysis highlighting the most commonly repeated words across the corpus (stop-words removed unless otherwise justified).
• Visual summaries (charts or heat maps) that illustrate noticeable spikes, dips, or shifts in vocabulary over chapters or sections.
• Brief narrative explaining what those trends suggest about tone, pacing, or thematic emphasis.
• All source code or reproducible notebooks so the process can be verified or rerun on future material.

Acceptance criteria
1. Report delivered as a PDF plus the original editable file (Word, Google Docs, or Markdown).
2. Visuals labeled and legible at both screen and print resolutions.
3. Scripts/notebooks execute without errors on a standard local machine (document any non-standard libraries).

If this sounds like a fit, let me know how quickly you can turn it around and which toolset you plan to use.