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project based on online sources of textual data such as newspaper archives, site user comment archives, digitized historical documents, or social media user comment archives.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.
|Edition description:||New Edition|
|Product dimensions:||7.37(w) x 9.12(h) x (d)|
About the Author
Gabe Ignatow is an Associate Professor of Sociology at the University of North Texas where he has taught since 2007. His research interests are in the areas of sociological theory, text mining and analysis methods, new media, and information policy.
Gabe’s current research involves working with computer scientists and statisticians to adapt text mining and topic modeling techniques for social science applications. Gabe has been working with mixed methods of text analysis since the 1990s, and has published this work in journals including Social Forces, Sociological Forum, Poetics, the Journal for the Theory of Social Behaviour, and the Journal of Computer-Mediated Communication. He is the author of over 30 peer-reviewed articles and book chapters and serves on the editorial boards of the journals Sociological Forum, the Journal for the Theory of Social Behaviour, and Studies in Media and Communications. He has served as the UNT Department of Sociology’s graduate program co-director and undergraduate program director and has been selected as a faculty fellow at the Center for Cultural Sociology at Yale University. He is also a co-founder and the CEO of Grad Trek, a graduate degree search engine company.
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.
Table of Contents
AcknowledgmentsPrefaceNote to the ReaderAbout the AuthorsPART I. FOUNDATIONSChapter 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 ReadingChapter 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 QuestionsChapter 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 ReadingChapter 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 ReadingPART II. RESEARCH DESIGN AND BASIC TOOLSChapter 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 ReadingChapter 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 QuestionsPART III. TEXT MINING FUNDAMENTALSChapter 7. Lexical Resources Learning Objectives Introduction Word Net Roget’s Thesaurus Linguistic Inquiry and Word Count General Inquirer Wikipedia Conclusion Key Terms Highlights Discussion TopicsChapter 8. Basic Text Processing Learning Objectives Introduction Basic Text Processing Language Models and Text Statistics More Advanced Text Processing Conclusion Key Terms Highlights Discussion TopicsChapter 9. Supervised Learning Learning Objectives Introduction Feature Representation and Weighting Supervised Learning Algorithms Evaluation of Supervised Learning Conclusion Key Terms Highlights Discussion TopicsPART IV. TEXT ANALYSIS METHODS FROM THE HUMANITIES AND SOCIAL SCIENCESChapter 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 ReadingChapter 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 ReadingChapter 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 ReadingPART V. TEXT MINING METHODS FROM COMPUTER SCIENCEChapter 13. Text Classification Learning Objectives Introduction What Is Text Classification? Applications of Text Classification Approaches to Text Classification Conclusion Key Terms Highlights Discussion TopicsChapter 14. Opinion Mining Learning Objectives Introduction What Is Opinion Mining? Resources for Opinion Mining Approaches to Opinion Mining Conclusion Key Terms Highlights Discussion TopicsChapter 15. Information Extraction Learning Objectives Introduction Entity Extraction Relation Extraction Web Information Extraction Template Filling Conclusion Key Terms Highlights Discussion TopicsChapter 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 ReadingPART VI. WRITING AND REPORTING YOUR RESEARCHChapter 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 ReadingAppendix A. Data Sources for Text MiningAppendix B. Text Preparation and Cleaning SoftwareAppendix C. General Text Analysis SoftwareAppendix D. Qualitative Data Analysis SoftwareAppendix E. Opinion Mining SoftwareAppendix F. Concordance and Keyword Frequency SoftwareAppendix G. Visualization SoftwareAppendix H. List of WebsitesAppendix I. Statistical ToolsGlossaryReferencesIndex