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.