Comparative study for Stylometric analysis techniques for authorship attribution

✍ Scribed by Raafat, Maryam A. (author);El-Wakil, Rania Abdel-Fattah (author);Atia, Ayman (author)

Publisher: IEEE
Year: 2021
Leaves: 6
Category: Scientific

No coin nor oath required. For personal study only.

✦ Synopsis

A text is a meaningful source of information. Capturing the right patterns in written text gives metrics to measure and infer to what extent this text belongs or is relevant to a specific author. This research aims to introduce a new feature that goes more in deep in the language structure. The feature introduced is based on an attempt to differentiate stylistic changes among authors according to the different sentence structure
each author uses. The study showed the effect of introducing this new feature to machine learning models to enhance their performance. It was found that the prediction of authors was enhanced by adding sentence structure as an additional feature as the f1 scores increased by 0.3% and when normalizing the data and adding the feature it increased by 5%.

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