An Introduction to Text Mining

Gabe Ignatow is Professor of Sociology and Director of Graduate Studies at the University of North Texas. His research interests are mainly in the areas of sociological theory, digital research methods, cognitive social science, and the philosophy of social science. His most recent books are Text Mining and An Introduction to Text Mining, both coauthored with Rada Mihalcea (University of Michigan). He is also a coeditor, with Wayne Brekhus (University of Missouri), of the Oxford Handbook of Cognitive Sociology. Rada Mihalcea is a professor of computer science and engineering at the University of Michigan. Her research interests are in computational linguistics, with a focus on lexical semantics, multilingual natural language processing, and computational social sciences. She serves or has served on the editorial boards of the following journals: Computational Linguistics, Language Resources and Evaluation, Natural Language Engineering, Research on Language and Computation, IEEE Transactions on Affective Computing, and Transactions of the Association for Computational Linguistics. She was a general chair for the Conference of the North American Chapter of the Association for Computational Linguistics (NAACL, 2015) and a program cochair for the Conference of the Association for Computational Linguistics (2011) and the Conference on Empirical Methods in Natural Language Processing (2009). She is the recipient of a National Science Foundation CAREER award (2008) and a Presidential Early Career Award for Scientists and Engineers (2009). In 2013, she was made an honorary citizen of her hometown of Cluj-Napoca, Romania.

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

"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 "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 "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 "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