Introduction to Text Analytics
eBook - ePub

Introduction to Text Analytics

A Guide for Digital Humanities & Social Sciences

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

Introduction to Text Analytics

A Guide for Digital Humanities & Social Sciences

About this book


This easy-to-follow book will revolutionise how you approach text mining and data analysis as well as equipping you with the tools, and confidence, to navigate complex qualitative data.

It can be challenging to effectively combine theoretical concepts with practical, real-world applications but this accessible guide provides you with a clear step-by-step approach.

Written specifically for students and early career researchers this pragmatic manual will: 

•             Contextualise your learning with real-world data and engaging case studies.

•             Encourage the application of your new skills with reflective questions.

•             Enhance your ability to be critical, and reflective, when dealing with imperfect data.

Supported by practical online resources, this book is the perfect companion for those looking to gain confidence and independence whilst using transferable data skills. 

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Yes, you can access Introduction to Text Analytics by Emily Öhman 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. Title Page
  4. Copyright Page
  5. Contents
  6. About this Book
  7. About the Author
  8. Online Resources
  9. Part I Basic Concepts and Tools for Text Analytics
  10. 1 Computational and Traditional Text Analysis
  11. 2 Basic Tools for Text Analytics
  12. 3 Dataset Creation and Considerations
  13. Part II Language and Computers
  14. 4 Language as Data
  15. 5 Regular Expressions
  16. Part III Programming for Text Analytics
  17. 6 Introduction to Python Programming
  18. 7 Preprocessing Textual Data
  19. 8 Data Manipulation and Exploration
  20. 9 Data Visualization
  21. Part IV Social Media Analytics
  22. 10 Text Mining
  23. 11 Social Media Analysis
  24. 12 The Basics of Machine Learning
  25. Part V Publishing
  26. 13 LaTeX Basics
  27. Acronyms
  28. Glossary
  29. References
  30. Index