This book offers a comprehensive and application-oriented exploration of Artificial Intelligence (AI) and Natural Language Processing (NLP), addressing both foundational principles and modern, data-driven methodologies. It is designed to equip readers with a deep understanding of how intelligent systems learn from data, interpret human language, and support automated decision-making across real-world contexts.

Natural Language Processing for Business and Organizations: Research and Innovation covers key areas such as machine learning, deep learning, text representation, language modeling, information extraction, sentiment analysis, and AI-driven analytics, while also discussing system design considerations for deploying NLP solutions at scale. Through carefully structured chapters, the book integrates theoretical insights with practical examples, case studies, and applied workflows, enabling readers to translate algorithms and models into effective AI applications. Written by a team of academic researchers and industry practitioners, the book emphasizes responsible and value-driven AI, including ethical considerations, data quality, and model evaluation.

This book is written for advanced undergraduate and postgraduate students, researchers, and professionals seeking to build, evaluate, and apply AI and NLP systems in academic, enterprise, and societal domains.

This book offers a comprehensive and application-oriented exploration of Artificial Intelligence (AI) and Natural Language Processing (NLP), addressing both foundational principles and modern, data-driven methodologies. It is designed to equip readers with a deep understanding of how intelligent systems learn from data, interpret human language, and support automated decision-making across real-world contexts.

Natural Language Processing for Business and Organizations: Research and Innovation covers key areas such as machine learning, deep learning, text representation, language modeling, information extraction, sentiment analysis, and AI-driven analytics, while also discussing system design considerations for deploying NLP solutions at scale. Through carefully structured chapters, the book integrates theoretical insights with practical examples, case studies, and applied workflows, enabling readers to translate algorithms and models into effective AI applications. Written by a team of academic researchers and industry practitioners, the book emphasizes responsible and value-driven AI, including ethical considerations, data quality, and model evaluation.

This book is written for advanced undergraduate and postgraduate students, researchers, and professionals seeking to build, evaluate, and apply AI and NLP systems in academic, enterprise, and societal domains.