04422nam 22008055 450 991098337740332120250214115229.09783031766312303176631810.1007/978-3-031-76631-2(MiAaPQ)EBC31908849(Au-PeEL)EBL31908849(CKB)37527870800041(DE-He213)978-3-031-76631-2(OCoLC)1500765733(EXLCZ)993752787080004120250214d2025 u| 0engurcnu||||||||txtrdacontentcrdamediacrrdacarrierNatural Language Analytics with Generative Large-Language Models A Practical Approach with Ollama and Open-Source LLMs /by Francisco S. Marcondes, Adelino Gala, Renata Magalhães, Fernando Perez de Britto, Dalila Durães, Paulo Novais1st ed. 2025.Cham :Springer Nature Switzerland :Imprint: Springer,2025.1 online resource (117 pages)SpringerBriefs in Computer Science,2191-57769783031766305 303176630X Introduction -- Natural Language Analytics -- Using Ollama -- Generative Prompt Engineering -- Case Study: LLM-based Anxiety Climate Index -- Conclusion.This book explores the application of generative Large Language Models (LLMs) for extracting and analyzing data from natural language artefacts. Unlike traditional uses of LLMs, such as translation and summarization, this book focuses on utilizing these models to convert unstructured text into data that can be processed through the data science pipeline to generate actionable insights. The content is designed for professionals in diverse fields including cognitive science, linguistics, management, and information systems. It combines insights from both industry and academia to provide a comprehensive understanding of how LLMs can be effectively used for natural language analytics (NLA). The book details practical methodologies for implementing LLMs locally using open-source tools, ensuring data privacy and feasibility without the need for expensive infrastructure. Key topics include interpretant, mindset and cultural analysis, emphasizing the use of LLMs to derive soft data—qualitative information crucial for nuanced decision-making. The text also outlines the technical aspects of LLMs, including their architecture, token embeddings, and the differences between encoder-based and decoder-based models. By providing a case study and practical examples, the authors show how LLMs can be used to meet various analytical needs, making this book a valuable resource for anyone looking to integrate advanced natural language processing techniques into their data analysis workflows.SpringerBriefs in Computer Science,2191-5776Natural language processing (Computer science)Artificial intelligenceData processingArtificial intelligenceBusinessData processingComputational linguisticsMachine learningNatural Language Processing (NLP)Data ScienceArtificial IntelligenceBusiness AnalyticsComputational LinguisticsMachine LearningNatural language processing (Computer science)Artificial intelligenceData processing.Artificial intelligence.BusinessData processing.Computational linguistics.Machine learning.Natural Language Processing (NLP).Data Science.Artificial Intelligence.Business Analytics.Computational Linguistics.Machine Learning.006.35Marcondes Francisco S1784298Gala Adelino1784299Magalhães Renata1784300Perez de Britto Fernando1784301Durães Dalila0Novais Paulo762363MiAaPQMiAaPQMiAaPQBOOK9910983377403321Natural Language Analytics with Generative Large-Language Models4315975UNINA