Part-of-speech (POS) tagging—the automatic assignment of grammatical categories to every token in a text corpus—is a foundational preprocessing step for AI-driven language applications such as machine translation, sentiment analysis, and information retrieval. For morphologically complex, low-resource languages such as Pashto, the scarcity of annotated data and standardised tools makes this task particularly challenging. This paper presents a systematic comparative evaluation of six machine learning (ML) and deep learning (DL) algorithms—Support Vector Machine (SVM), Decision Tree (DT), Random Forest (RF), K-Nearest Neighbor (KNN), Multi-Layer Perceptron (MLP), and Naïve Bayes (NB)—... More >

Stance detection identifies a text’s position or attitude toward a given subject. A major challenge in Roman Urdu is the lack of a publicly available dataset for political stance detection. To address this gap, we constructed a high-quality dataset of 8,374 political tweets and comments using the Twitter API, annotated with stance labels: agree, disagree, and unrelated. The dataset captures diverse political viewpoints and user interactions. For feature representation, we employed TF-IDF due to its effectiveness in handling high-dimensional, context-sensitive Roman Urdu text. Several machine learning classifiers were evaluated, with Random Forest achieving the highest accuracy of 95%. Addi... More >