Text Data Management and Analysis A Practical Introduction to Information Retrieval and Text Mining 1st edition by ChengXiang Zhai, Sean Massung ISBN 1970001178 9781970001174

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ISBN 10: 1970001178
ISBN 13: 9781970001174
Author: ChengXiang Zhai, Sean Massung

Recent years have seen a dramatic growth of natural language text data, including web pages, news articles, scientific literature, emails, enterprise documents, and social media such as blog articles, forum posts, product reviews, and tweets. This has led to an increasing demand for powerful software tools to help people analyze and manage vast amounts of text data effectively and efficiently. Unlike data generated by a computer system or sensors, text data are usually generated directly by humans, and are accompanied by semantically rich content. As such, text data are especially valuable for discovering knowledge about human opinions and preferences, in addition to many other kinds of knowledge that we encode in text. In contrast to structured data, which conform to well-defined schemas (thus are relatively easy for computers to handle), text has less explicit structure, requiring computer processing toward understanding of the content encoded in text. The current technology of natural language processing has not yet reached a point to enable a computer to precisely understand natural language text, but a wide range of statistical and heuristic approaches to analysis and management of text data have been developed over the past few decades. They are usually very robust and can be applied to analyze and manage text data in any natural language, and about any topic.

This book provides a systematic introduction to all these approaches, with an emphasis on covering the most useful knowledge and skills required to build a variety of practically useful text information systems. The focus is on text mining applications that can help users analyze patterns in text data to extract and reveal useful knowledge. Information retrieval systems, including search engines and recommender systems, are also covered as supporting technology for text mining applications. The book covers the major concepts, techniques, and ideas in text data mining and information retrieval from a practical viewpoint, and includes many hands-on exercises designed with a companion software toolkit (i.e., MeTA) to help readers learn how to apply techniques of text mining and information retrieval to real-world text data and how to experiment with and improve some of the algorithms for interesting application tasks. The book can be used as a textbook for a computer science undergraduate course or a reference book for practitioners working on relevant problems in analyzing and managing text data.

Text Data Management and Analysis A Practical Introduction to Information Retrieval and Text Mining 1st Table of contents:

PART I. OVERVIEW AND BACKGROUND

1. Introduction

2. Background

3. Text Data Understanding

4. MeTA: A Unified Toolkit for Text Data Management and Analysis

PART II. TEXT DATA ACCESS

5. Overview of Text Data Access

6. Retrieval Models

7. Feedback

8. Search Engine Implementation

9. Search Engine Evaluation

10. Web Search

11. Recommender Systems

PART III. TEXT DATA ANALYSIS

12. Overview of Text Data Analysis

13. Word Association Mining

14. Text Clustering

15. Text Categorization

16. Text Summarization

17. Topic Analysis

18. Opinion Mining and Sentiment Analysis

19. Joint Analysis of Text and Structured Data

PART IV. UNIFIED TEXT DATA MANAGEMENT ANALYSIS SYSTEM

20. Toward a Unified System for Text Managment and Analysis

App. A. Bayesian Statistics

App. B. Expectation-Maximization

App C. KL-divergence and Dirichlet Prior Smoothing

References

Index

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ChengXiang Zhai,Sean Massung,Text Data,Information Retrieval,Text Mining