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

By Bjorn Halversen

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 dataApply deep learning architectures, attention mechanisms, and transformers to textDevelop named entity recognition and information extraction systemsPerform sentiment analysis and topic modeling across large text collectionsBuild semantic search systems using document embeddings and vector databasesDesign Retrieval-Augmented Generation (RAG) systems grounded in organizational dataUnderstand when to prompt, fine-tune, or use parameter-efficient fine-tuning techniquesAdapt NLP and LLM solutions for specialized domains and business workflowsIntegrate AI models through enterprise APIs and automated processesEvaluate accuracy, faithfulness, feedback loops, and business impactFrom 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.

• Format: Paperback
• Language: English
• ISBN: B0HH8VM7VQ
• ISBN13: 9798170563791
• Release Date: August 2026
• Publisher: Independently Published
• Length: 206 Pages
• Weight: 0.81 lbs.
• Dimensions: 0.4" x 7.0" x 10.0"