Saifullah Razali, University of Hertfodshire, Singapore
Figurative language: metaphor, simile, idiom, hyperbole, sarcasm, and irony encodes meaning that often departs from literal interpretation and is crucial across literature, social media, and educational content. Detecting such language remains challenging for NLP systems because it requires pragmatic, cultural and world knowledge. This paper presents a thorough study of figurative language detection using pretrained language models (PLMs). We review linguistic foundations, describe architectures and training strategies using PLMs (BERT, RoBERTa, and GPT-style models), present an experimental framework, and report results drawing on recent benchmark datasets and shared tasks.
Figurative Language, Metaphor Detection, Sarcasm Detection, Pretrained Language Models, BERT, RoBERTa, GPT
Zinia Rahman1, Wang Zheng1, Refat Khan Pathan2 1School of Automation, Department of Control Science and Engineering, Southeast University Nanjing, China 2School of Computing and Artificial Intelligence, 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 Convolutional Neural Network (CNN) 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, emotion classification reached perfect accuracy across all models, while 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, CNN, ML
