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An Introduction to Text Mining
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An Introduction to Text Mining
Research Design, Data Collection, and Analysis

Additional resources:


December 2017 | 344 pages | SAGE Publications, Inc
 
Acknowledgments
 
Preface
 
Note to the Reader
 
About the Authors
 
PART I. FOUNDATIONS
 
Chapter 1. Text Mining and Text Analysis
Learning Objectives

 
Introduction

 
Six Approaches to Text Analysis

 
Challenges and Limitations of Using Online Data

 
Conclusion

 
Key Terms

 
Highlights

 
Review Questions

 
Discussion Questions

 
Developing a Research Proposal

 
Further Reading

 
 
Chapter 2. Acquiring Data
Learning Objectives

 
Introduction

 
Online Data Sources

 
Advantages and Limitations of Online Digital Resources for Social Science Research

 
Examples of Social Science Research Using Digital Data

 
Conclusion

 
Key Term

 
Highlights

 
Discussion Questions

 
 
Chapter 3. Research Ethics
Learning Objectives

 
Introduction

 
Respect for Persons, Beneficence, and Justice

 
Ethical Guidelines

 
Institutional Review Boards

 
Privacy

 
Informed Consent

 
Manipulation

 
Publishing Ethics

 
Conclusion

 
Key Terms

 
Highlights

 
Review Questions

 
Discussion Questions

 
Web Resources

 
Developing a Research Proposal

 
Further Reading

 
 
Chapter 4. The Philosophy and Logic of Text Mining
Learning Objectives

 
Introduction

 
Ontological and Epistemological Positions

 
Metatheory

 
Making Inferences

 
Conclusion

 
Key Terms

 
Highlights

 
Discussion Questions

 
Internet Resources

 
Developing a Research Proposal

 
Further Reading

 
 
PART II. RESEARCH DESIGN AND BASIC TOOLS
 
Chapter 5. Designing Your Research Project
Learning Objectives

 
Introduction

 
Critical Decisions

 
Idiographic and Nomothetic Research

 
Levels of Analysis

 
Qualitative, Quantitative, and Mixed Methods Research

 
Choosing Data

 
Formatting Your Data

 
Conclusion

 
Key Terms

 
Highlights

 
Review Questions

 
Discussion Questions

 
Developing a Research Proposal

 
Further Reading

 
 
Chapter 6. Web Scraping and Crawling
Learning Objectives

 
Introduction

 
Web Statistics

 
Web Crawling

 
Web Scraping

 
Software for Web Crawling and Scraping

 
Conclusion

 
Key Terms

 
Highlights

 
Discussion Questions

 
 
PART III. TEXT MINING FUNDAMENTALS
 
Chapter 7. Lexical Resources
Learning Objectives

 
Introduction

 
WordNet

 
Roget’s Thesaurus

 
Linguistic Inquiry and Word Count

 
General Inquirer

 
Wikipedia

 
Conclusion

 
Key Terms

 
Highlights

 
Discussion Topics

 
 
Chapter 8. Basic Text Processing
Learning Objectives

 
Introduction

 
Basic Text Processing

 
Language Models and Text Statistics

 
More Advanced Text Processing

 
Conclusion

 
Key Terms

 
Highlights

 
Discussion Topics

 
 
Chapter 9. Supervised Learning
Learning Objectives

 
Introduction

 
Feature Representation and Weighting

 
Supervised Learning Algorithms

 
Evaluation of Supervised Learning

 
Conclusion

 
Key Terms

 
Highlights

 
Discussion Topics

 
 
PART IV. TEXT ANALYSIS METHODS FROM THE HUMANITIES AND SOCIAL SCIENCES
 
Chapter 10. Analyzing Narratives
Learning Objectives

 
Introduction

 
Approaches to Narrative Analysis

 
Planning a Narrative Analysis Research Project

 
Qualitative Narrative Analysis

 
Mixed Methods and Quantitative Narrative Analysis Studies

 
Conclusion

 
Key Terms

 
Highlights

 
Review Questions

 
Developing a Research Proposal

 
Further Reading

 
 
Chapter 11. Analyzing Themes
Learning Objectives

 
Introduction

 
How to Analyze Themes

 
Examples of Thematic Analysis

 
Conclusion

 
Key Terms

 
Highlights

 
Review Questions

 
Developing a Research Proposal

 
Further Reading

 
 
Chapter 12. Analyzing Metaphors
Learning Objectives

 
Introduction

 
Cognitive Metaphor Theory

 
Approaches to Metaphor Analysis

 
Qualitative, Quantitative, and Mixed Methods

 
Conclusion

 
Key Terms

 
Highlights

 
Review Questions

 
Developing a Research Proposal

 
Further Reading

 
 
PART V. TEXT MINING METHODS FROM COMPUTER SCIENCE
 
Chapter 13. Text Classification
Learning Objectives

 
Introduction

 
What Is Text Classification?

 
Applications of Text Classification

 
Approaches to Text Classification

 
Conclusion

 
Key Terms

 
Highlights

 
Discussion Topics

 
 
Chapter 14. Opinion Mining
Learning Objectives

 
Introduction

 
What Is Opinion Mining?

 
Resources for Opinion Mining

 
Approaches to Opinion Mining

 
Conclusion

 
Key Terms

 
Highlights

 
Discussion Topics

 
 
Chapter 15. Information Extraction
Learning Objectives

 
Introduction

 
Entity Extraction

 
Relation Extraction

 
Web Information Extraction

 
Template Filling

 
Conclusion

 
Key Terms

 
Highlights

 
Discussion Topics

 
 
Chapter 16. Analyzing Topics
Learning Objectives

 
Introduction

 
What Are Topic Models?

 
How to Use Topic Models

 
Examples of Topic Modeling

 
Conclusion

 
Key Terms

 
Highlights

 
Review Questions

 
Developing a Research Proposal

 
Internet Resources

 
Further Reading

 
 
PART VI. WRITING AND REPORTING YOUR RESEARCH
 
Chapter 17. Writing and Reporting Your Research
Learning Objectives

 
Introduction: Academic Writing

 
Evidence and Theory

 
The Structure of Social Science Research Papers

 
Conclusion

 
Key Terms

 
Highlights

 
Web Resources

 
Undergraduate Research Journals

 
Further Reading

 
 
Appendix A. Data Sources for Text Mining
 
Appendix B. Text Preparation and Cleaning Software
 
Appendix C. General Text Analysis Software
 
Appendix D. Qualitative Data Analysis Software
 
Appendix E. Opinion Mining Software
 
Appendix F. Concordance and Keyword Frequency Software
 
Appendix G. Visualization Software
 
Appendix H. List of Websites
 
Appendix I. Statistical Tools
 
Glossary
 
References
 
Index

Supplements

Instructor Teaching Site

Password-protected Instructor Resources include:

  • Editable, chapter-specific Microsoft® PowerPoint® slides that offer you complete flexibility in easily creating a multimedia presentation for your course. 
  • Author-written assignments and activities, including accompanying data sets, can be used as in-class exercises, homework assignments, or exam questions.
Student Study Site
The open-access Student Study Site includes downloadable data sets selected by the authors for use with assignments and activities.

“This is a comprehensive book on a timely and important research method for social scientific research. Researchers who want to learn the development of text mining methods and learn how to integrate the methods into their research projects will find this book beneficial.”

Kenneth C. C. Yang
The University of Texas at El Paso

“In the age of big data, this text is an excellent introduction to text mining for undergraduates and beginning graduate students. The proliferation of text as data particularly in social media require the inclusion of this topic in the data analysis toolkit of the social scientist.”

A. Victor Ferreros
Florida State University

“This is an excellent book that covers a broad range of topics on text analysis. Examples from a variety of disciplines are used, making the text useful to students across the social sciences, humanities, and sciences and also accessible to those who do not have a deep background in this area.”

Jennifer Bachner
Johns Hopkins University

“This book provides an excellent base for budding data scientists and provides tools, methods and references that will be extremely useful in their work. Methods from various disciplines are discussed in detail and provide a wonderful base for building business appropriate data mining projects.”

Roger D. Clark
NWN Corporation

I was looking for a book in Text Mining geared towards undergraduates in computer science. This book does have several chapters that would be geared towards comp sci students, but it's not sufficient. However, the book would be more useful for the humanities to get an understanding of how to apply text mining along with a research-focused approach of the book, while learning some useful methods from computer science.

Dr Paula Lauren
Arts and Science, Lawrence Technological Univ
May 31, 2018

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