Text Mining
eBook - ePub

Text Mining

A Guidebook for the Social Sciences

  1. English
  2. ePUB (mobile friendly)
  3. Available on iOS & Android
eBook - ePub

Text Mining

A Guidebook for the Social Sciences

About this book

Online communities generate massive volumes of natural language data and the social sciences continue to learn how to best make use of this new information and the technology available for analyzing it. Text Mining brings together a broad range of contemporary qualitative and quantitative methods to provide strategic and practical guidance on analyzing large text collections. This accessible book, written by a sociologist and a computer scientist, surveys the fast-changing landscape of data sources, programming languages, software packages, and methods of analysis available today. Suitable for novice and experienced researchers alike, the book will help readers use text mining techniques more efficiently and productively.

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Yes, you can access Text Mining by Gabe Ignatow,Rada Mihalcea in PDF and/or ePUB format, as well as other popular books in Social Sciences & Social Science Research & Methodology. We have over one million books available in our catalogue for you to explore.

Table of contents

  1. Cover
  2. Half Title
  3. Publisher Note
  4. Title Page
  5. Copyright Page
  6. Brief Contents
  7. Detailed Contents
  8. Preface
  9. Acknowledgments
  10. About the Authors
  11. Part I Digital Texts, Digital Social Science
  12. 1 Social Science and the Digital Text Revolution
  13. 2 Research Design Strategies
  14. Part II Text Mining Fundamentals
  15. 3 Web Crawling and Scraping
  16. 4 Lexical Resources
  17. 5 Basic Text Processing
  18. 6 Supervised Learning
  19. Part III Text Analysis Methods From the Humanities and Social Sciences
  20. 7 Thematic Analysis, Qualitative Data Analysis Software, and Visualization
  21. 8 Narrative Analysis
  22. 9 Metaphor Analysis
  23. Part IV Text Mining Methods From Computer Science
  24. 10 Word and Text Relatedness
  25. 11 Text Classification
  26. 12 Information Extraction
  27. 13 Information Retrieval
  28. 14 Sentiment Analysis
  29. 15 Topic Models
  30. Part V Conclusions
  31. 16 Text Mining, Text Analysis, and the Future of Social Science
  32. References
  33. Index