Bridging Linguistics Divides: Integrating Artificial Intelligence Technologies into the Practice of Comparative Literary Criticism

Abstract

This study investigates the integration of artificial intelligence (AI) technologies into the practice of comparative literary criticism, aiming to bridge linguistic divides and enhance analytical capabilities. The research is guided by the following research questions: How can AI facilitate the analysis of linguistic features across different texts? In what ways can machine learning algorithms contribute to the identification of thematic and stylistic similarities and differences? What are the implications of these technologies for traditional literary criticism? To address these questions, mixed-methods approach combining qualitative and quantitative analyses was used. The methodology includes the use of natural language processing (NLP) tools to conduct textual analysis on a diverse corpus of literary works from various cultural contexts. Machine learning algorithms are applied to identify patterns in language use, thematic development, and stylistic elements, with a focus on both stylistic metrics and semantic networks. Data visualization techniques are utilized to present findings in an accessible manner, highlighting the relationships and divergences among the texts. The objectives of this research are threefold: first, to develop a framework for integrating AI tools into literary analysis; second, to assess the effectiveness of these tools in uncovering linguistic and thematic connections; and third, to critically evaluate the ethical considerations and limitations inherent in using AI within the humanities. By fostering a dialogue between linguistics and literary studies, this research aspires to contribute to a more nuanced understanding of comparative literature, ultimately enriching the field with innovative methodologies and perspectives.