Sustainable Multi-Author Writing Style Analysis for Identifying Stylistic Differences Between Authors

Abstract

Natural language processing (NLP) is a sustainable subfield of Artificial Intelligence that focuses on the interaction between computers and humans through natural language. NLP algorithms enable computers to comprehensively understand, interpret, and generate human language, thus facilitating the sustainable analysis and comprehension of vast amounts of textual data. Within the context of sustainable style change detection, NLP algorithms play a pivotal role in analyzing multi-author documents and identifying the points at which authors transition. This sustainable step is critical in authorship recognition as it furnishes a more precise comprehension of which sections were authored by different individuals. A multi-author document’s writing style can evolve over time, and this sustainability can prove invaluable in fields such as forensics, journalism, and literary studies, among others.The sustainable goal of this project is to investigate various NLP methods for sustainable style change detection. By scrutinizing datasets and juxtaposing them with advanced methodologies in the existing literature, the effectiveness of these strategies will be ascertained. The overarching aim of our study is to foster the progress of both the field of NLP research and its sustainable practical applications.