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An Introduction to Text Mining
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An Introduction to Text Mining
Research Design, Data Collection, and Analysis

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October 2017 | 336 pages | SAGE Publications, Inc

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

 

Gabe Ignatow and Rada Mihalcea's An Introduction to Text Mining: Research Design, Data Collection, and Analysis 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.

 
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  
Conclusions  
Key Terms  
Highlights  
Student Study Site  
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  
Conclusions  
Key Terms  
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  
Conclusions  
Key Terms  
Highlights  
Student Study Site  
Review Questions  
Discussion Questions  
Web Resources  
Developing Your Research Proposal  
Further Reading  
 
Chapter 4 The Philosophy and Logic of Text Mining
Learning Objectives  
Introduction  
Ontological and Epistemological Positions  
Metatheory  
Making Inferences  
Conclusions  
Key Terms  
Highlights  
Student Study Site  
Discussion Questions  
Internet Resources  
Developing Your 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  
Conclusions  
Key Terms  
Highlights  
Student Study Site  
Review Questions  
Discussion Questions  
Developing Your 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  
 
Part III: Text Mining Fundamentals
 
Chapter 7 Lexical Resources
Learning Objectives  
Introduction  
WordNet  
WordNet Affect  
Roget’s Thesaurus  
Linguistic Inquiry and Word Count  
General Inquirer  
Wikipedia  
Conclusions  
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  
Conclusions  
Key Terms  
Highlights  
Discussion Topics  
 
Chapter 9 Supervised Learning
Learning Objectives  
Introduction  
Feature Representation and Weighting  
Supervised Learning Algorithms  
Evaluation of Supervised Learning  
Conclusions  
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  
Conclusions  
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  
Conclusions  
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  
Conclusions  
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  
Conclusions  
Key Terms  
Highlights  
Discussion Topics  
 
Chapter 14 Opinion Mining
Learning Objectives  
Introduction  
What Is Opinion Mining?  
Resources for Opinion Mining  
Approaches to Opinion Mining  
Conclusions  
Key Terms  
Highlights  
Discussion Topics  
 
Chapter 15 Information Extraction
Learning Objectives  
Introduction  
Entity Extraction  
Relation Extraction  
Web Information Extraction  
Template Filling  
Conclusions  
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  
Conclusions  
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  
Conclusions  
Key Terms  
Highlights  
Student Study Site  
Web Resources  
Undergraduate Research Journals  
Further Reading  
 
Glossary
 
References
 
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

Supplements

Instructor Teaching Site

Password-protected Instructor Resources include editable, chapter-specific Microsoft® PowerPoint® slides that offer you complete flexibility in easily creating a multimedia presentation for your course as well as assignments and activities based on data sets. 

“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
Key features

KEY FEATURES:

  • Foundational chapters introduce critical conceptual and practical tools needed before data collection.
  • Comprehensive survey of major approaches to text mining allow instructors to focus their course on the optimal and appropriate methods.
  • Research in the Spotlight features introduce students to interesting contemporary research that uses text mining tools.
  • Research Proposal Development features encourage students to think through the implications of lessons from each chapter for their own research projects.

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ISBN: 9781506337005