An Introduction to Text Mining

Gabe Ignatow - University of North Texas, USA
Rada Mihalcea - University of Michigan, USA

October 2017 | 344 pages | SAGE Publications, Inc

Available Formats

ISBN: 9781506337005	Paperback	Suggested Retail Price: $75.00	Bookstore Price: $60.00
ISBN: 9781506337012	Electronic Version	Suggested Retail Price: $60.90	Bookstore Price: $48.72

See what’s new to this edition by selecting the Features tab on this page. Should you need additional information or have questions regarding the HEOA information provided for this title, including what is new to this edition, please email sageheoa@sagepub.com. Please include your name, contact information, and the name of the title for which you would like more information. For information on the HEOA, please go to http://ed.gov/policy/highered/leg/hea08/index.html.

For assistance with your order: Please email us at textsales@sagepub.com or connect with your SAGE representative.

SAGE
2455 Teller Road
Thousand Oaks, CA 91320
www.sagepub.com

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.

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

June 17, 2018

Report this review

Key features

KEY FEATURES:

Foundational chapters introduce critical conceptual and practical tools needed before data collection.
Comprehensive survey of major approaches to text mining allow instructors to focus their course on the optimal and appropriate methods.
Research in the Spotlight features introduce students to interesting contemporary research that uses text mining tools.
Research Proposal Development features encourage students to think through the implications of lessons from each chapter for their own research projects.

Get 30% off SAGE Campus’ online course: Introduction to Text Mining for Social Scientists
Learn from course authors, Gabe Ignatow and Rada Mihalcea, on this self-paced online course. The course takes between 6-8 hours to complete is perfect for social scientists who want to gain a conceptual overview of the text mining landscape to take first steps towards working on a text mining project or collaborating with computational colleagues. Simply use the discount code TXTMBOOK30 at the checkout.

Sample Materials & Chapters

Chapter 1: Text Mining and Text Analysis

Chapter 6: Web Scraping and Crawling

You are here

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

Supplements

Sample Materials & Chapters

For instructors

Select a Purchasing Option