Applied Text Analytics for the Enterprise: Implementing Deep Learning and Natural Language Processing with LLM

Your organization is sitting on a massive source of intelligence buried in text. Contracts, customer feedback, reports, documents, support conversations, and other unstructured data contain valuable information. Applied Text Analytics for the Enterprise shows you how to turn that information into practical business intelligence using modern NLP, deep learning, and Large Language Models (LLMs). Designed for professionals working with enterprise data and AI, this practical guide takes you from modern text-processing fundamentals to sophisticated production applications. Inside, you'll learn how to: - Build scalable NLP and text analytics workflows for enterprise data- Apply deep learning architectures, attention mechanisms, and transformers to text- Develop named entity recognition and information extraction systems- Perform sentiment analysis and topic modeling across large text collections- Build semantic search systems using document embeddings and vector databases- Design Retrieval-Augmented Generation (RAG) systems grounded in organizational data- Understand when to prompt, fine-tune, or use parameter-efficient fine-tuning techniques- Adapt NLP and LLM solutions for specialized domains and business workflows- Integrate AI models through enterprise APIs and automated processes- Evaluate accuracy, faithfulness, feedback loops, and business impact From unstructured data to actionable intelligence, this book connects advanced AI techniques with the practical realities of enterprise implementation. Build smarter text analytics systems and unlock more value from the information your organization already has.