Published Paper Details:

Because digital content is growing so quickly, sentiment analysis (SA) is now an important tool for figuring out how people feel and sorting through text data. Natural language processing (NLP) has come a long way, but low-resource languages, especially Sindhi, still haven't been studied enough because there aren't enough computational tools and annotated datasets. This study fills this gap by presenting the Sindhi News Headlines Dataset (SNHD), a new collection of data that has been labelled for both SA and category classification in eight areas: Crime, Economy, Entertainment, Health, Politics, Science & Technology, Social, and Sports. We compare different machine learning (ML), deep learning (DL), and transformer-based methods on SA and category classification tasks to see how well they work. We also use Explainable Artificial Intelligence (XAI) methods like Local Interpretable Model-Agnostic Explanations (LIME) to learn more about how models make decisions. The SNHD dataset shows that traditional ML models work better than DL and transformer-based models in experiments. Support Vector Machines with Radial Basis Function (SVM-RBF) is the best for SA (0.74 accuracy and weighted F-score), and the Ridge Classifier (RC) is the best for category classification (0.84 accuracy and weighted F-score). XLM-RoBERTa is one of the best transformer models for category classification, with an accuracy of 0.82 and a weighted F-score. These results set a standard for future research in Sindhi NLP and show how hybrid methods could help with problems that come up with low-resource languages. This work is a basic resource for NLP researchers who want to improve computational methods for Sindhi and other lesser-known languages.