Xiang Zhai C.-X., Massung S. Text Data Management and Analysis: A Practical Introduction to Information Retrieval and Text Mining

New York: Association for Computing Machinery and Morgan & Claypool, 2016. — 531 p. — ISBN: 978-1-97000-119-8.The growth of “big data” created unprecedented opportunities to leverage computational and statistical approaches to turn raw data into actionable knowledge that can support various application tasks. This is especially true for the optimization of decision making in virtually all application domains such as health and medicine, security and safety, learning and education, scientific discovery, and business in telligence. Just as a microscope enables us to see things in the “micro world” and a telescope allows us to see things far away, one can imagine a “big data scope” would enable us to extend our perception ability to “see” useful hidden information and knowledge buried in the data, which can help make predictions and improve the optimality of a chosen decision. This book covers general computational techniques for managing and analyzing large amounts of text data that can help users manage and make use of text data in all kinds of applications.
This book consists of four parts. Part I provides an overview of the content covered in the book and some background knowledge needed to understand the chapters later. Parts II and III contain the major content of the book and cover a wide range of techniques in IR (called Text Data Access techniques) and techniques in TM (called Text Data Analysis techniques), respectively. Part IV summarizes the book with a unified framework for text management and analysis where many techniques of IR and TM can be combined to provide more advanced support for text data access and analysis with humans in the loop to control the workflow.Overview and BackgroundText Data Understanding
META: A Unified Toolkit for Text Data Management and Analysis
Text Data Access
Overview of Text Data Acces
Retrieval Models
Feedback
Search Engine Implementation
Search Engine Evaluation
Web Search
Recommender Systems
Text Data Analysis
Overview of Text Data Analysis
Word Association Mining
Text Clustering
Text Categorization
Text Summarization
Topic Analysis
Opinion Mining and Sentiment Analysis
Joint Analysis of Text and Structured Data
Unified Text Data Management Analysis System
Toward A Unified System for Text Management and Analysis
Appendix A. Bayesian Statistics
Appendix B. Expectation- Maximization
Appendix C. KL-divergence and Dirichlet Prior Smoothing