An Investigation of Supervised Learning Methods for Authorship Attribution in Short Hinglish Texts using Char & Word N-grams

TL;DR

This study evaluates supervised learning techniques for authorship attribution in short Hinglish texts from WhatsApp, finding SVM and Naive Bayes most effective with specific n-gram features, achieving over 94% accuracy.

Contribution

It compares multiple supervised models and feature types for authorship attribution in short, code-mixed texts, highlighting the effectiveness of word unigrams and character 3-grams.

Findings

SVM achieved up to 95.08% accuracy.

Naive Bayes achieved up to 94.45% accuracy.

Word unigrams and character 3-grams are most effective.

Abstract

The writing style of a person can be affirmed as a unique identity indicator; the words used, and the structuring of the sentences are clear measures which can identify the author of a specific work. Stylometry and its subset - Authorship Attribution, have a long history beginning from the 19th century, and we can still find their use in modern times. The emergence of the Internet has shifted the application of attribution studies towards non-standard texts that are comparatively shorter to and different from the long texts on which most research has been done. The aim of this paper focuses on the study of short online texts, retrieved from messaging application called WhatsApp and studying the distinctive features of a macaronic language (Hinglish), using supervised learning methods and then comparing the models. Various features such as word n-gram and character n-gram are compared via methods viz., Naive Bayes Classifier, Support Vector Machine, Conditional Tree, and Random Forest, to find the best discriminator for such corpora. Our results showed that SVM attained a test accuracy of up to 95.079% while similarly, Naive Bayes attained an accuracy of up to 94.455% for the dataset. Conditional Tree & Random Forest failed to perform as well as expected. We also found that word unigram and character 3-grams features were more likely to distinguish authors accurately than other features.