Stylometry Approach for Detecting Writing Style Patterns and Changes in Poetry Text

This project analyzes the stylistic changes in the poetry of Maya Angelou across different periods of her career using unsupervised learning and natural language processing techniques. The focus is on extracting and comparing stylistic topic modeling to understand the evolution of her writing style.

Tech Stack 💻

• Python: Core programming language used for the project.
• NLTK: Used for text preprocessing and feature extraction.
• roBERTa and Huggingface Transformers: Employed for semantic analysis and sentiment detection.
• Gensim: Utilized for topic modeling and analysis.

Features 🏆

• Basic Text Features: Analyzes document length, mean sentence length, mean word length, and readability.
• Lexical Usage: Examines lexical richness, function word frequencies, and content word frequencies.
• Semantic Analysis: Detects semantic repetition and performs sentiment analysis (polarity and strength).
• Punctuation Usage: Analyzes the use of punctuation in the text.
• Writing Style Patterns: Uses unsupervised learning techniques (KNN and PCA) to detect writing style patterns.

Deployment 🌐

• Local Environment

• Visual Studio Code

Usage 🎯

• Open your terminal and run the following command: git clone https://github.com/danieladam7/writing-patterns-and-changing-detection
• cd https://github.com/danieladam7/writing-patterns-and-changing-detection
• pip install -r requirements.txt
• python main.py

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