Quantitative Text Analysis Using R
eBook - ePub

Quantitative Text Analysis Using R

Scraping, Preparing, Visualising and Modelling Data

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

Quantitative Text Analysis Using R

Scraping, Preparing, Visualising and Modelling Data

About this book

Grounded in examples from across the social sciences, this book walks you through the process of doing quantitative text analysis step by step. Clear and accessible, it empowers you to progress from beginner level to understanding and using computational social science concepts with ease. Covering key steps in the research process like ethics, data collection, and model choice, it helps you develop important research skills – and equips you with the programming tools you need to handle text data without error.

The textbook offers R software guidance at an easy-to-follow pace, the book presents the coding skills you need to collect and prepare data, providing a strong foundation as you move into data analysis. It will:

·       Help you develop key data skills like cleaning, managing, classifying and visualizing data

·       Encourage your ability to be critical and reflective when dealing with data

·       Offer clear guidance on using messy, real-world data and big data from sources like Wikipedia

Supported by practical online resources including extensive coding examples and software guidance, this book will give you confidence in applying your programming skills and enable you to take control of handling textual data in your own research. 

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Yes, you can access Quantitative Text Analysis Using R by Julian Bernauer,Anna Wohlmann 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. Preface
  7. Acknowledgements
  8. About the Authors
  9. Meet the Case Study Contributors
  10. GitHub Repository
  11. 1 Calculating with Letters
  12. 2 Using R for Text Analysis
  13. 3 Text as Data: Obtaining, Preparing and Cleaning
  14. 4 Extracting and Visualising Information from Text
  15. 5 Supervised Machine Learning for Text Data
  16. 6 Unsupervised Machine Learning for Text Data
  17. 7 Evaluation and Validation of Quantitative Text Analysis
  18. 8 Using Python within R for Quantitative Text Analysis
  19. 9 Communicating Text Analysis
  20. References
  21. Index