Students in social science courses communicate, socialize, shop, learn, and work online. When they are asked to collect data for course projects they are often drawn to social media platforms and other online sources of textual data. There are many software packages and programming languages available to help students collect data online, and there are many texts designed to help with different forms of online research, from surveys to ethnographic interviews. But there is no textbook available that teaches students how to construct a viable research project based on online sources of textual…mehr
Students in social science courses communicate, socialize, shop, learn, and work online. When they are asked to collect data for course projects they are often drawn to social media platforms and other online sources of textual data. There are many software packages and programming languages available to help students collect data online, and there are many texts designed to help with different forms of online research, from surveys to ethnographic interviews. But there is no textbook available that teaches students how to construct a viable research project based on online sources of textual data such as newspaper archives, site user comment archives, digitized historical documents, or social media user comment archives. Gabe Ignatow and Rada F. Mihalcea's new text An Introduction to Text Mining will be a starting point for undergraduates and first-year graduate students interested in collecting and analyzing textual data from online sources, and will cover the most critical issues that students must take into consideration at all stages of their research projects, including: ethical and philosophical issues; issues related to research design; web scraping and crawling; strategic data selection; data sampling; use of specific text analysis methods; and report writing.Hinweis: Dieser Artikel kann nur an eine deutsche Lieferadresse ausgeliefert werden.
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.
Inhaltsangabe
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
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
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