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

Understand

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.