An Introduction to Text Mining: Research Design, Data Collection, and Analysis
Autor Gabe Ignatow, Rada F. Mihalceaen Limba Engleză Paperback – 20 dec 2017
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Specificații
ISBN-13: 9781506337005
ISBN-10: 1506337007
Pagini: 344
Dimensiuni: 187 x 232 x 18 mm
Greutate: 0.56 kg
Ediția:1
Editura: SAGE Publications
Colecția Sage Publications, Inc
Locul publicării:Thousand Oaks, United States
ISBN-10: 1506337007
Pagini: 344
Dimensiuni: 187 x 232 x 18 mm
Greutate: 0.56 kg
Ediția:1
Editura: SAGE Publications
Colecția Sage Publications, Inc
Locul publicării:Thousand Oaks, United States
Recenzii
“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.”
“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.”
“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.”
“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.”
“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.”
“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.”
“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.”
Cuprins
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
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
Notă biografică
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.
Descriere
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.