Deep Learning and BERT-Based Models for Poetry Analysis: A Study on Chronological and Subject-Wise Categorization of Tagore’s Poetic Works - Ruma, Jannatul Ferdous; Sultana, Jannat; Akter, Sharmin; Laboni, Jesrin Jahan; Rahman, Rashedur M. (SN computer science, 2025)
In the modern era, where artificial intelligence (AI) intersects with computational literary studies, the analysis of poetry presents both challenges and opportunities. Rabindranath Tagore’s poetry holds immense literary and historical significance, yet understanding its contextual motivations and systematically categorizing it remains complex. This study pioneers a chronological classification of Tagore’s poetry while also exploring subject-based categorization through advanced computational methods. Utilizing an AI-driven approach that integrates machine learning (ML), deep learning (DL), and natural language processing (NLP), we evaluated three ML models with two embedding methods, four DL models with four embedding methods, and five large language models (LLMs). Our methodology achieved 85.99% accuracy in subject-based classification using LSTM and BiLSTM, while chronological classification with BanglaBERT reached 75.91% accuracy. This work establishes new benchmarks in Bengali poetry analysis, demonstrating that domain-specific adaptations consistently out-perform general-purpose LLMs. Moreover, the integration of Explainable AI with LIME enhances model interpretability, bridging computational techniques with literary critique. Our findings contribute significantly to computational literary studies, offering innovative methods for exploring culturally and linguistically rich poetic contexts.