Roman Urdu sentiment analysis faces significant challenges due to transliteration inconsistencies, informal language usage, and the lack of labeled datasets. This study proposes a novel framework that addresses these challenges by combining advanced data preprocessing techniques and data augmentation strategies such as synonym replacement, back-translation, and random word insertion. These methods enhance dataset diversity, improving the model’s generalization ability. A rich Roman Urdu dataset was collected from diverse sources, including social media platforms (Facebook, Twitter, YouTube), blogs, forums, and e-commerce sites, to capture a wide range of user opinions. Three deep learning... More >

The process of assigning grammatical categories, such as ``Noun'' and ``Verb,'' to every word in a text corpus is known as part-of-speech (POS) tagging. This technique is widely used in applications like sentiment analysis, machine translation, and other linguistic and computational tasks. However, the unique features of the Pashto language and its limited resources present significant challenges for POS tagging. This study explores the critical role of POS tagging in the Pashto language by employing six popular deep-learning and machine-learning techniques. Experimental results demonstrate machine learning methods' effectiveness in capturing Pashto text's grammatical patterns. The evaluatio... More >