A Machine Learning Approach to the Analytics of Representations of Violence in Khaled Hosseini's Novels

Abstract

Over the past two decades, literary works from Afghanistan have sought to depict the extensive and increasing level of violence directed against individuals and communities. This study aims to identify the different types of violence represented in selected literary works from the country. To this end, an artificial intelligence approach—comprised of opinion mining and hybrid machine learning models—is developed as a method of analysis. The Afghan American writer Khaled Hosseini's novels—The Kite Runner (2003), A Thousand Splendid Suns (2007), and And the Mountains Echoed (2017)—are used as primary data sources. As a theoretical framework, the study benefits from the Norwegian sociologist Johan Galtung's theory of conflict triangle—direct violence, structural violence, and cultural violence. To classify the sentiments related to types of violence in the selected novels, the study utilizes sentiment analysis and machine learning algorithms.

Decoding violence against women: analysing harassment in middle eastern literature with machine learning and sentiment analysis

“…Through the application of quantitative methods and computational power, these studies aim to uncover insights regarding the structure, trends, and patterns within the literature. Computational literary studies encompass various disciplines such as computational linguistics, statistical methodology, natural language processing, machine learning, and text mining (Da, 2019;Elmi et al, 2023;Mohd Amram et al, 2023, Zhao and. The field of digital humanities offers diverse and substantial perspectives on social situations.…”