Conference Paper2023Cited by 5

Genre Classification of Bangla Poem Using Machine Learning and Deep Learning Techniques

Syed Tangim Pasha, Ashraful Islam, Mohammed Masudur Rahman, Eshtiak Ahmed, Md Ferdouse Ahmed Foysal, Md Zahangir Alam

2023 IEEE World AI IoT Congress (AIIoT)

IEEE, pp. 0236–0242

DOI: 10.1109/aiiot58121.2023.10174592URL

Abstract

The computational analysis of the Bangla poems is a challenging task due to the diverse linguistic, stylistic, and semantic features of the Bangla language. In this work, we prepared a dataset of 1311 Bangla poems of two separate categories: Love and Miscellaneous poem, which contain 500 and 811 poems respectively. We used word or semantic-based features to classify Bangla poems using the TF-IDF feature techniques. We used Logistic Regression, Naïve Bayes (NB), and Support Vector Machine (SVM) models for classification through machine learning, and we used Bayesian optimization techniques for hyperparameters tuning of these three models. We also used LSTM, CNN, and transformer models for this research. For the performance evaluation of the classification models, we used four evaluation metrics of precision, recall, F1-score, and accuracy. We also used the ROC-AUC curve to distinguish between all the machine learning and deep learning models. The experimental results expressed that, the transformer model achieved the highest accuracy compared to all the typical machine learning and deep learning models with an accuracy of 87%.

Keywords

  • TF-IDF features
  • love poetry
  • transformer models
  • genre classification
  • convolutional neural networks
  • Bengali language
  • LSTM
  • text classification
  • Bayesian hyperparameter optimization

CCDS Authors

Syed Tangim Pasha
Syed Tangim Pasha

Syed Tangim Pasha

  • Collaborator, HCI Wing

Lecturer

Department of Computing and Information System

Daffodil International University

Published at

UbiComp

Research Interests

Health Informatics, Affective Computing, Digital Health, Wearable Computing

Ashraful Islam, PhD
Ashraful Islam

Ashraful Islam, PhD

  • Center Director, CCDS
  • Director, HCI Wing

Assistant Professor

Department of Computer Science and Engineering

Independent University, Bangladesh (IUB)

Published at

CHI, UIST, UbiComp, IJCNN, ICIP, HCII, ICDMW, BLP, IEEE Internet of Things Journal, JMIR, JMIR Human Factors, JMIR Formative Research, IEEE Access, Smart Agricultural Technology, Diabetes Research and Clinical Practice, PLOS ONE

Research Interests

Human-Computer Interaction, AI for Social Good, AI for Public Health, AI for Impact

Eshtiak Ahmed
Eshtiak Ahmed

Eshtiak Ahmed

  • Collaborator, HCI Wing

Doctoral Researcher

Gamification Group

Tampere University, Finland

Published at

UbiComp, ICIP, HCII, IEEE Access

Research Interests

Human-Computer Interaction, Human-Robot Interaction, Gamification and Assistive Technologies

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Cited By

  1. 1.Rayhanuzzaman, Tanjim Mahmud, Utpol Kanti Das, Sultana Rokeya Naher, Mohammad Shahadat Hossain, Karl Andersson. (2024). Investigating the Effectiveness of Deep Learning and Machine Learning for Bangla Poems Genre Classification. , 1–6[10.1109/conit61985.2024.10627555]
  2. 2.BADRIYYA B. AL-ONAZI, MAJDY M. ELTAHIR, MUHAMMAD SWAILEH A. ALZAIDI, SHOUKI A. EBAD, SHOAYEE DLAIM ALOTAIBI, AHMED SAYED. (2024). AUTOMATING METER CLASSIFICATION OF ARABIC POEMS: A HARRIS HAWKS OPTIMIZATION WITH DEEP LEARNING PERSPECTIVE. Fractals, 33(02)[10.1142/s0218348x25400080]
  3. 3.Danish Haikal Bin Suhaimi, Hasiah Mohamed, Nurul Izzah Irdina Binti Mohd Kairudin, Syarifah Bahiyah Rahayu. (2025). Malay Pantun Theme Classification Using Support Vector Machine. , 324–329[10.1109/icset65917.2025.11283995]
  4. 4.Bikokhita Dutta, Mandira Neog, Nomi Baruah. (2024). Unveilling the Effectiveness of LSTM with TF-IDF in Assamese Poem Classification:A Comparative Analysis with Conventional and Deep Learning Approaches. , 1–6[10.1109/icccnt61001.2024.10725384]
  5. 5.B. Lavanya, R. Sowmiya. (2025). Exploring the Strength of Extensive Features in Short Poem Genre Classification Using Advanced Feature Engineering. Lecture notes in networks and systems, 241–260[10.1007/978-3-031-84460-7_16]
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