MACHINE LEARNING METHODS FOR SOLVING TEXT AUTHOR IDENTIFICATION PROBLEMS
Keywords:
Machine learning, text author identification, natural language processing, feature extraction, supervised learning, unsupervised learning, deep learning.Abstract
Text author identification is a crucial problem in natural language processing (NLP), with applications ranging from forensic analysis to copyright enforcement and literary studies. Machine learning (ML) has emerged as a powerful tool for addressing this challenge, offering algorithms capable of analyzing stylistic, lexical, and syntactic features in text. This paper explores the state-of-the-art ML methods for solving text author identification problems, including supervised, unsupervised, and deep learning techniques. A comprehensive review of the literature is presented, highlighting the effectiveness of various approaches. Additionally, the discussion outlines challenges such as data sparsity, feature selection, and ethical considerations. Experimental results demonstrate the impact of advanced ML models on classification accuracy and scalability. The findings emphasize the growing importance of machine learning in author attribution research.
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