A Hybrid Neural Network Approach for Author Classification in Written Articles

Tarandeep Kaur; Gagandeep Singh

doi:10.71097/IJSAT.v16.i2.6112

A Hybrid Neural Network Approach for Author Classification in Written Articles

Author(s)	Tarandeep Kaur, Gagandeep Singh
Country	India
Abstract	With the exponential growth of digital content, accurately determining the authorship of textual material has become increasingly critical. Authorship attribution, the task of determining the writer of a given text, has emerged as an important area of research within the domains of Natural Language Processing (NLP) and Machine Learning (ML). This paper presents an approach for classifying the authorship of written articles using NLP techniques and supervised ML algorithms. Initially, a dataset comprising text samples from three distinct authors was prepared. The text data was pre-processed to remove noise, and essential features were extracted using the TF-IDF technique. These features were then utilized to train and evaluate three supervised classifiers: Support Vector Machine (SVM), Naïve Bayes, and Logistic Regression. The performance of each classifier was assessed using accuracy, precision, recall, and F1-score metrics. Among the models tested, the SVM classifier achieved the highest accuracy of 94%. The results demonstrate that the proposed approach is effective for authorship classification and holds promise for applications in digital forensics, content verification, and intellectual property protection.
Keywords	Enhanced MLP, Information gain, Decision Trees, Neural Networks
Field	Computer > Artificial Intelligence / Simulation / Virtual Reality
Published In	Volume 16, Issue 2, April-June 2025
Published On	2025-06-09
DOI	https://doi.org/10.71097/IJSAT.v16.i2.6112
Short DOI	https://doi.org/g9pz8j

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International Journal on Science and Technology

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A Hybrid Neural Network Approach for Author Classification in Written Articles

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