Social media sites generate massive volumes of natural language data that are available for social science research. But social scientists have struggled to take advantage of "big data," and of the new technologies available for analyzing it. Should researchers learn programming languages in order to mine textual data? Are there software packages that can be repurposed for social science research? Can traditional theories and methods be scaled up to take advantage of new sources of textual data, or are new methods and new ways of thinking about theory needed? Text Mining: A Guidebook for Social Sciences addresses these questions and provides a methods guidebook to text mining and analysis for social scientists. It is intended for both new and experienced researchers, and provides strategic as well as practical guidance in the areas of text mining and qualitative and quantitative text analytic research methods. Gabe Ignatow and Rada Mihalcea critically survey this fast-changing landscape, providing a roadmap for researchers that will shorten the time from concept to publication, and scholarly impact.

Preface Acknowledgments About the Authors Part I Digital Texts, Digital Social Science chapter 1. Social Science and the Digital Text Revolution chapter 2. Research Design Strategies Part II Text Mining Fundamentals chapter 3. Web Crawling and Scraping chapter 4. Lexical Resources chapter 5. Basic Text Processing chapter 6. Supervised Learning Part III Text Analysis Methods from the Humanities and Social Sciences chapter 7. Thematic Analysis, Qualitative Data Analysis Software, and Visualization chapter 8. Narrative Analysis chapter 9. Metaphor Analysis Part IV Text Mining Methods from Computer Science chapter 10. Word and Text Relatedness chapter 11. Text Classification chapter 12. Information Extraction chapter 13. Information Retrieval chapter 14. Sentiment Analysis chapter 15. Topic Models V Conclusions chapter 16. Text Mining, Text Analysis, and the Future of Social Science References Index