Analyzing Textual Information
eBook - ePub

Analyzing Textual Information

From Words to Meanings through Numbers

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

Analyzing Textual Information

From Words to Meanings through Numbers

About this book

Researchers in the social sciences and beyond are dealing more and more with massive quantities of text data requiring analysis, from historical letters to the constant stream of content in social media. Traditional texts on statistical analysis have focused on numbers, but this book will provide a practical introduction to the quantitative analysis of textual data. Using up-to-date R methods, this book will take readers through the text analysis process, from text mining and pre-processing the text to final analysis. It includes two major case studies using historical and more contemporary text data to demonstrate the practical applications of these methods. Currently, there is no introductory how-to book on textual data analysis with R that is up-to-date and applicable across the social sciences. Code and a variety of additional resources to enrich the use of this book are available on an accompanying website. These resources include data files from the 39th Congress, and also the collection of tweets of President Trump, now no longer available to researchers via Twitter itself.

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Yes, you can access Analyzing Textual Information by Johannes Ledolter,Lea S. VanderVelde 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. Series
  3. Publisher Note
  4. Half Title
  5. Series
  6. Acknowledgements
  7. Title Page
  8. Copyright Page
  9. Table of Contents
  10. Series Editor’s Introduction
  11. Preface
  12. Acknowledgments
  13. About the Authors
  14. Chapter 1 Introduction
  15. Chapter 2 A Description of the Studied Text Corpora and a Discussion of Our Modeling Strategy
  16. Chapter 3 Preparing Text for Analysis: Text Cleaning and Formatting
  17. Chapter 4 Word Distributions: Document-Term Matrices of Word Frequencies and the “Bag of Words” Representation
  18. Chapter 5 Metavariables and Text Analysis Stratified on Metavariables
  19. Chapter 6 Sentiment Analysis
  20. Chapter 7 Clustering of Documents
  21. Chapter 8 Classification of Documents
  22. Chapter 9 Modeling Text Data: Topic Models
  23. Chapter 10 n-Grams and Other Ways of Analyzing Adjacent Words
  24. Chapter 11 Concluding Remarks
  25. Appendix Listing of Website Resources
  26. Index