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Writing Styles Classification Using Stylometric Analysis

A Python application that divides a document into sentence-based chunks, extracts stylometric features and uses K-Means clustering to group chunks by writing style. It chooses the cluster count with the Elbow Method.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Writing Styles Classification Using Stylometric Analysis — OpenViST project profile in AI Data & Analytics.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it to explore stylistic variation within one document when clustering passages by measurable text features is more useful than assigning each passage a known author label.

Who it is for

  • Researchers exploring stylometry and unsupervised text analysis.
  • Readers investigating stylistic changes across passages in a document.
  • Developers studying a Python implementation of within-document style clustering.

Documented uses

  • Group passages that share similar stylometric characteristics.
  • Explore shifts in style that may reflect different authors or genres.
  • Identify passages for human review in an intrinsic plagiarism investigation.
Before you choose

Requirements & limits

The code is available in a public GitHub repository under the MIT license.

  • The analysis works by dividing text into fixed-size sentence chunks, so results depend on chunking and the selected stylometric features.
  • K is selected with the Elbow Method; the resulting cluster count is treated by the project as corresponding to the document’s number of styles.
  • The README reports an experiment involving two writing styles, while describing plagiarism detection as a possible application rather than a definitive finding.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Analyze a document for writing-style differences

Use the described stylometric workflow to group text chunks by writing style.

Before you start

  • A single document to analyze; the README says the system can be run on any document.
  • The document is divided into chunks using a fixed number of sentences per chunk.

Steps

  1. Divide the document into chunks using a fixed sentence-count window.

  2. Compute stylometric feature vectors for each chunk, including lexical, vocabulary-richness, and readability features.

  3. Cluster the feature vectors with K-Means, selecting the cluster count K using the Elbow Method.

  4. Review which chunks group together as indicators of shared writing style.

Expected outcome

Chunks with similar stylometric features are grouped together; the number of clusters is used to indicate the number of writing styles in the document.

Guide limitations

  • The README describes the analysis method but does not provide execution commands or operational setup instructions.
  • The described approach identifies stylistic groupings; any plagiarism interpretation requires human review.
Documentation 1 ↗
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
15 October 2026
Original website title
Hassaan-Elahi/Writing-Styles-Classification-Using-Stylometric-Analysis · Official homepage title · retained in its source language
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