Comparative study for Stylometric analysis techniques for authorship attribution
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Comparative study for Stylometric analysis techniques for authorship attribution
Raafat, M.A
;
El-Wakil, R.A-F
;
Atia, A
Full Text link:
https://qrgo.page.link/yM2PY
Date issued:
5/27/2021
Scientific Journal Rankings:
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Series Info:
International Mobile, Intelligent, and Ubiquitous Computing Conference, MIUCC;Pages 176 - 181
Type:
Article
Keywords:
authorship attribution , Constituent Analysis , Deep Learning , Machine Learning , NLP , Stylometry , text classification
Abstract:
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%. © 2021 IEEE.
Description:
Scopus
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Faculty Of Computer Science Research Paper
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