Text Mining: Concepts, Implementation, and Big Data Challenge

by Taeho Jo

Overview

This popular book, updated as a textbook for classroom use, discusses text mining and different ways this type of data mining can be used to find implicit knowledge from text collections. The author provides the guidelines for implementing text mining systems in Java, as well as concepts and approaches. The book starts by providing detailed text preprocessing techniques and then goes on to provide concepts, the techniques, the implementation, and the evaluation of text categorization. It then goes into more advanced topics including text summarization, text segmentation, topic mapping, and automatic text management. The book features exercises and code to help readers quickly learn and apply knowledge.

Product Details

ISBN-13:	9783031759758
Publisher:	Springer Nature Switzerland
Publication date:	12/15/2024
Series:	Studies in Big Data , #45
Edition description:	Second Edition 2024
Pages:	447
Product dimensions:	6.10(w) x 9.25(h) x (d)

About the Author

Dr. Taeho Jo works as a faculty member for school of game in Hongik University, South Korea. He received his PhD from University of Ottawa in 2006. His research spans text mining, neural networks, machine learning, and information retrieval. He has four years’ experience working for industrial organizations and ten years’ experience working for in academia. He has published almost 150 research papers, and he was awarded two times in the world wide biography dictionary, “Marquis Who’s Who in the World”.

Table of Contents

Part I: Foundation.- Introduction.- Text Indexing.- Text Encoding.- Text Association.- Part II: Text Categorization.- Text Categorization: Conceptual View.- Text Categorization: Approaches.- Text Categorization: Implementation.- Text Categorization: Evaluation.- Part III: Text Clustering.- Text Clustering: Conceptual View.- Text Clustering: Approaches.- Text Clustering: Implementation.- Text Clustering: Evaluation.- Part IV: Advanced Topics.- Text Summarization.- Text Segmentation.- Taxonomy Generation.- Dynamic Document Organization.- References.- Index.