Deep Learning and BERT-Based Models for Poetry Analysis: A Study on Chronological and Subject-Wise Categorization of Tagore’s Poetic Works
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 s...
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| Published in: | SN computer science Vol. 6; no. 7; p. 904 |
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| Main Authors: | , , , , |
| Format: | Journal Article |
| Language: | English |
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Singapore
Springer Nature Singapore
15.10.2025
Springer Nature B.V |
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| ISSN: | 2661-8907, 2662-995X, 2661-8907 |
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| Abstract | 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. |
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| AbstractList | 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. |
| ArticleNumber | 904 |
| Author | Rahman, Rashedur M. Ruma, Jannatul Ferdous Laboni, Jesrin Jahan Akter, Sharmin Sultana, Jannat |
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| Cites_doi | 10.1109/iSemantic59612.2023.10295293 10.15503/jecs2022.1.285.298 10.1109/ICACCI.2014.6968484 10.1007/978-3-642-21786-9_37 10.1109/ACCESS.2020.2987842 10.18653/v1/P19-4007 10.18653/v1/2022.findings-naacl.98 10.52866/ijcsm.2022.02.01.014 10.17562/PB-44-12 10.18653/v1/W16-1609 10.18653/v1/2020.findings-emnlp.445 10.1093/llc/fqae014 10.12928/telkomnika.v17i5.12646 10.3390/bdcc7040183 10.1007/s13369-020-04388-8 10.1007/978-3-319-23678-0 |
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| SubjectTerms | Accuracy Arabic language Arabic literature Classification Computer Imaging Computer Science Computer Systems Organization and Communication Networks Data Structures and Information Theory Datasets Deep learning Embedding Explainable artificial intelligence Information Systems and Communication Service Large language models Machine learning Natural language processing Original Research Pattern Recognition and Graphics Poetry Poets Semantic analysis Semantics Software Engineering/Programming and Operating Systems Support vector machines Tagore, Rabindranath (1861-1941) Vision |
| Title | Deep Learning and BERT-Based Models for Poetry Analysis: A Study on Chronological and Subject-Wise Categorization of Tagore’s Poetic Works |
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