AI-Driven Literary Analysis: Exploring the Impact of Artificial Intelligence on Text Interpretation and Criticism

Artificial Intelligence (AI) in literary studies has disrupted traditional schools of thought regarding textual analysis, interpretation, and criticism. This research establishes a framework that is AI-powered: the Literary Interpretative Neural Algorithm (LINA), which shows promise for the analysis of complex linguistic patterns, thematic structures, and stylistic elements in teaching and learning language. With its hybrid approach integrating Natural Language Processing (NLP), transformer-based deep learning models, and sentiment analysis, LINA assesses literary texts ranging across historical and contemporary genres. Contrasting the conventional methods of literary analysis often judged through the lens of subjective interpretation, LINA enables data-driven, unbiased analyses of themes, character development, and intertextual relationships. The research further examines the capability of AI to reveal those aspects that have remained obscure: to establish hidden patterns, authorial intent, and the evolution of genre over aeons. The effectiveness of the model is validated in contrast to a heterogeneous corpus of literary works, including insights derived from the proposed model against traditional critical methods. This study concludes by emphasizing that AI-enhanced literary analysis could serve to advance academic discourse, automate the tasks of literary classification, and provide additional layers for text interpretation. Contributions will lie at the interaction between AI and humanities in translation and publications, stressing the need for interdisciplinary approaches in the digital age. Future work will characterize refined AI approaches for deeper semantic understanding and ethical issues in automated literary criticism.