An introduction to text mining : research design, data collection, and analysis

著者

    • Ignatow, Gabe
    • Mihalcea, Rada

書誌事項

An introduction to text mining : research design, data collection, and analysis

Gabe Ignatow, Rada Mihalcea

Sage, c2018

  • : pbk

大学図書館所蔵 件 / 全9件

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注記

Includes bibliographical references (p. 289-306) and index

内容説明・目次

内容説明

This is the ideal introduction for students seeking to collect and analyze textual data from online sources. It covers the most critical issues that they must take into consideration at all stages of their research projects.

目次

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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