What is Stylometry?

Stylometry is the application of the study of linguistic style, usually to written language, but it has successfully been applied to music and to fine-art paintings as well.

Stylometry is often used to attribute authorship to anonymous or disputed documents. It has legal as well as academic and literary applications, ranging from the question of the authorship of Shakespeare's works to forensic linguistics.

Why write a library?

After searching through a wide variety of libraries for Python I was unable to find one that seemed to fit the needs.

I also found a research paper that I really like that talked about applying machine learning techniques and stylometry. I decided to write a simple library while learning more about the NLTK python library.

The initial version of the software took only about 3 hours and allowed me to extract a wide variety of features from text data. The library has since been extended into several different facets

Setting Up

1. System Dependendencies (Mac Specific)

brew install graphviz

1. install necessary packages from requirements.txt:

pip install -r requirements.txt

1. install nltk punkt tokenizer:

import nltk
nltk.download('punkt')

1. If you get this error Couldn't import dot_parser, loading of dot files will not be possible.

pip uninstall pydot
pip uninstall pyparsing
pip install -Iv https://pypi.python.org/packages/source/p/pyparsing/pyparsing-1.5.7.tar.gz
pip install pydot

Data Preparation

1. Download Text from Gutenberg Project
2. Clean up Text files removing table of contents, licenses, etc
3. Split the file into 1000 line samples using split -l 1000 hamlet.txt -d to rename the output files consider using split --numeric-suffixes=1 --additional-suffix=.csv -l 1000 hamlet.txt hamlet_
4. To rebuild the original data just cat book-0.txt book-1.txt book-2.txt > entire-book.txt

I tried to make sure that every author have a similar number of lines and samples to analyze.

Demo

Download the repositories

$ git clone git@github.com:jpotts18/stylometry.git
$ git clone git@github.com:jpotts18/stylometry-data.git

$ ipython

Extract stylometry features from one document

from stylometry.extract import *
dickens1 = StyloDocument('stylometry-data/Dickens/tale-two-cities-0.txt')
dickens1.text_output()

Extract stylometry features from a set of documents called a corpus

import stylometry

# Single Author Corpus
dickens_corpus = StyloCorpus.from_glob_pattern('stylometry-data/Dickens/*.txt')
dickens_corpus.output_csv('/Users/jpotts18/Desktop/dickens.csv')

# All authors
novel_corpus = StyloCorpus.from_glob_pattern('stylometry-data/*/*.txt')
novel_corpus.output_csv('/Users/jpotts18/Desktop/novels.csv')

Decision Tree Classificaiton

from stylometry.classify import *
# splits data into validation and training default 80% train 20% validation
dtree = StyloDecisionTree('/Users/jpotts18/Desktop/novels.csv')
# fit the decision tree to the data
dtree.fit()
# predict the authorship of the validation set
dtree.predict()
# Show the confusion matrix and accuracy of the validation prediction
dtree.confusion_matrix()
# Write the decision tree to an image file
dtree.write_tree('tree.png')

Clustering and PCA

from stylometry.cluster import *
# Create a KMeans clusterer and run PCA on the data
kmeans = StyloKMeans('/Users/jpotts18/Desktop/novels.csv')
# Cluster the PCA'd data using K-means
kmeans.fit()
# Shot the plot of explained variance per principle component
kmeans.stylo_pca.plot_explained_variance()
# Show the plot of the PCA'd data with the cluster centroids
kmeans.plot_clusters()