Document Type : Original Article
Author
Assistant Professor, Department of English Language and Literature, Velayat University, Iranshahr, Iran
Abstract
This study aims to philosophically reinterpret literary symbolism in the poetry of William Butler Yeats by designing and implementing an interdisciplinary framework grounded in theories of the philosophy of literature and deep learning algorithms. Yeats, as a prominent Neo-Platonic and symbolist poet of the twentieth century, constructs a complex system of multilayered symbols and cyclical structures that reflect fundamental ontological concepts, including plural identity, reflective consciousness, and existential contradictions. Focusing on the philosophical analysis of these structures, the research seeks to develop a data-driven model for uncovering nonlinear relationships between symbolic elements and philosophical concepts within poetic texts. The methodology involves selecting and preprocessing a curated corpus of Yeats’s symbolic poems, performing semantic annotation, and modeling conceptual sequences using Recurrent Neural Networks (RNNs) with Long Short-Term Memory (LSTM) architecture and Attention mechanisms within the framework of Natural Language Processing. These models, capable of identifying intricate semantic dependencies, recurring sequences, and multi-level structures, enable a more precise and systematic analysis of philosophical symbolism. Findings indicate that integrating deep learning algorithms with theoretical foundations in the philosophy of literature not only overcomes the limitations of traditional interpretive methods but also provides a conceptual basis for redefining symbolism as an epistemological and ontological system. This data-driven approach enhances the philosophical analysis of literary texts and contributes to methodological innovation in theoretical and applied literary studies within the emerging field of digital humanities.
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