Zinia Rahman ¹ , Wang Zheng ¹ , Refat Khan Pathan ² , ¹ Southeast University Nanjing, China, ¹ Faculty of Engineering and Technology Sunway University, Malaysia

Automatic interpretation of poetry presents significant challenges for natural language processing due to figurative language, cultural symbolism, and subtle emotional cues. This study proposes a comparative computational framework for extracting themes and emotions from English and Bangla poems using TF-IDF features and multiple supervised algorithms. Support Vector Machines (SVM), k-Nearest Neighbors (KNN), Decision Trees (DT), Random Forests (RF) - and a Multilayer Perceptron (MLP)were evaluated for both thematic and emotional categorization. For English poetry, ensemble and margin-based models achieved the highest performance, with SVM and Random Forest attaining up to 88.7% accuracy for emotion and 85.5% for theme classification. In Bangla poetry, theme classification remained highly discriminative, with Random Forest achieving 94% accuracy.The study demonstrates the effectiveness of traditional machine learning approaches for bilingual poetic analysis in low-resource literary domains.

Poetry Analysis, Emotion and theme classification, Deep Learning, MLP, ML