Whether you are a graduate student taking your first steps in NLP, a data scientist building text classification systems, a researcher working on low-resource languages, or a practitioner deploying content moderation tools, this book gives you everything you need — from foundational principles to advanced deep learning architectures — explained through the lens of a genuine research problem with real-world consequences. Starting from the very basics of what Natural Language Processing is and why it matters, the book walks you through every stage of a professional NLP pipeline with clarity, depth, and purpose: You will learn how to collect social media data responsibly and at scale — designing keyword lexicons, building crawling pipelines, and applying ethical data handling practices that respect user privacy and platform policies. You will master data cleaning and preprocessing for one of NLP's most challenging domains — informal social media text in a morphologically rich, low-resource language. The twelve-step Urdu preprocessing pipeline developed in this research handles everything from Unicode normalization and Arabic-Urdu character confusion to Roman Urdu spelling variation and code-mixed content that switches between Urdu and English mid-sentence. You will understand data annotation from the ground up — how to design annotation guidelines that produce consistent, reliable labels, how to recruit and train annotators for sensitive content tasks, and how to measure inter-annotator agreement using Cohen's Kappa and Fleiss' Kappa. The annotated dataset at the heart of this research achieved a Fleiss' Kappa of 91.5% — almost perfect agreement — setting a standard for future work. You will explore feature extraction across the full spectrum of modern NLP — from traditional Bag of Words, TF-IDF, and n-gram representations through dense semantic embeddings with Word2Vec and FastText to contextual transformer-based representations with Urdu-BERT and Urdu-RoBERTa. And you will understand not just how each technique works, but when to use it — and when simpler approaches genuinely outperform more sophisticated ones.