An Introduction to Text Mining

Research Design, Data Collection, and Analysis
Gabe Ignatow
Rada F. Mihalcea
An Introduction to Text Mining
October 2017 | 344 pages | Sage US
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Description

“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

Teach students how to construct a viable research project based on online sources.

Gabe Ignatow and Rada Mihalcea's An Introduction to Text Mining provides a foundation for readers seeking a solid introduction to mining text data. The book covers the most critical issues that must be taken into consideration for research projects, including web scraping and crawling, strategic data selection, data sampling, use of specific text analysis methods, and report writing. In addition to covering technical aspects of various approaches to contemporary text mining and analysis, the book covers ethical and philosophical dimensions of text-based research and social science research design.

Instructors, sign into study.sagepub.com/ignatow for additional resources!



Contents

Acknowledgments

Acknowledgments

Preface

Preface

Note to the Reader

Note to the Reader

About the Authors

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 A. Data Sources for Text Mining

Appendix B. Text Preparation and Cleaning Software

Appendix B. Text Preparation and Cleaning Software

Appendix C. General Text Analysis Software

Appendix C. General Text Analysis Software

Appendix D. Qualitative Data Analysis Software

Appendix D. Qualitative Data Analysis Software

Appendix E. Opinion Mining Software

Appendix E. Opinion Mining Software

Appendix F. Concordance and Keyword Frequency Software

Appendix F. Concordance and Keyword Frequency Software

Appendix G. Visualization Software

Appendix G. Visualization Software

Appendix H. List of Websites

Appendix H. List of Websites

Appendix I. Statistical Tools

Appendix I. Statistical Tools

Glossary

Glossary

References

References

Index

Index

Description

“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

Teach students how to construct a viable research project based on online sources.

Gabe Ignatow and Rada Mihalcea's An Introduction to Text Mining provides a foundation for readers seeking a solid introduction to mining text data. The book covers the most critical issues that must be taken into consideration for research projects, including web scraping and crawling, strategic data selection, data sampling, use of specific text analysis methods, and report writing. In addition to covering technical aspects of various approaches to contemporary text mining and analysis, the book covers ethical and philosophical dimensions of text-based research and social science research design.

Instructors, sign into study.sagepub.com/ignatow for additional resources!



Contents

Acknowledgments

Acknowledgments

Preface

Preface

Note to the Reader

Note to the Reader

About the Authors

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 A. Data Sources for Text Mining

Appendix B. Text Preparation and Cleaning Software

Appendix B. Text Preparation and Cleaning Software

Appendix C. General Text Analysis Software

Appendix C. General Text Analysis Software

Appendix D. Qualitative Data Analysis Software

Appendix D. Qualitative Data Analysis Software

Appendix E. Opinion Mining Software

Appendix E. Opinion Mining Software

Appendix F. Concordance and Keyword Frequency Software

Appendix F. Concordance and Keyword Frequency Software

Appendix G. Visualization Software

Appendix G. Visualization Software

Appendix H. List of Websites

Appendix H. List of Websites

Appendix I. Statistical Tools

Appendix I. Statistical Tools

Glossary

Glossary

References

References

Index

Index

SAGE Publishing Logo

An Introduction to Text Mining

Research Design, Data Collection, and Analysis

Gabe Ignatow , Rada F. Mihalcea
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October 2017 | 344 pages | Sage US

Format Published Date ISBN Price

“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

Teach students how to construct a viable research project based on online sources.

Gabe Ignatow and Rada Mihalcea's An Introduction to Text Mining provides a foundation for readers seeking a solid introduction to mining text data. The book covers the most critical issues that must be taken into consideration for research projects, including web scraping and crawling, strategic data selection, data sampling, use of specific text analysis methods, and report writing. In addition to covering technical aspects of various approaches to contemporary text mining and analysis, the book covers ethical and philosophical dimensions of text-based research and social science research design.

Instructors, sign into study.sagepub.com/ignatow for additional resources!




Table Of Contents:

  • 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

Recent Product Reviews:

“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

Recommendations