Automatic theme and motif identification in large-scale English literary corpora using deep learning approaches

Abstract

The identification of themes and motifs in literary texts is a fundamental aspect of literary analysis, traditionally performed through manual annotation and expert interpretation. However, the increasing availability of large-scale English literary corpora presents new challenges and opportunities for automated analysis. This paper proposes a deep learning (DL)-based framework for automatically detecting themes and motifs in extensive literary collections. The dataset comprises diverse sources, including classic literature, modern fiction, and poetry, ensuring a broad representation of thematic structures. A rigorous preprocessing pipeline is applied, involving stop word removal and tokenization to refine textual data. For feature extraction, Word2Vec is utilized to capture semantic relationships between words. The core novelty of this research lies in the implementation of a Duelist Algorithm-optimized Bi-directional Long Short-Term Memory (DAO-BiLSTM) model, which enhances the model’s ability to detect and classify recurring thematic elements with high accuracy. The proposed method achieves an accuracy of 96.24%, recall of 97.32%, precision of 95.6%, and an F1-score of 94.7%, demonstrating superior performance over existing methods. The model is implemented in Python 3.9 using TensorFlow in a high-performance computing environment, ensuring efficient processing of large-scale textual data. Experimental results illustrate the effectiveness of the proposed approach in identifying complex motifs and themes across various literary genres. These findings highlight the potential of DL in augmenting literary analysis, enabling large-scale, data-driven thematic exploration that complements traditional human-driven methodologies.