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Analyzing Textual Information
From Words to Meanings through Numbers
- Johannes Ledolter - The University of Iowa, USA
- Lea S. VanderVelde - The University of Iowa, USA
Volume:
188
Courses:
Big Data | Intermediate/Advanced Research Methods | Quantitative Methods | Research Methods & Statistics in Sociology | Research Methods in Mass Communication | Research Methods in Political Science | Research Methods in Political Science | Research Methods in Sociology | Statistics - General Interest |
Big Data | Intermediate/Advanced Research Methods | Quantitative Methods | Research Methods & Statistics in Sociology | Research Methods in Mass Communication | Research Methods in Political Science | Research Methods in Political Science | Research Methods in Sociology | Statistics - General Interest |
June 2021 | 144 pages | SAGE Publications, Inc
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 are available on an accompanying website for the book.
Chapter 1: Introduction to Doing Qualitative Research in a Digital World
Chapter 2: A description of the studied text corpora and a discussion of our modeling strategy
Chapter 3: Preparing text for analysis. Text Cleaning and formatting
Chapter 4: Word distributions: Document-term matrices of word frequencies and the “Bag of Words” representation
Chapter 5: Meta variables and the text analysis stratified on meta variables
Chapter 6: Sentiment analysis
Chapter 7: Clustering of documents
Chapter 8: Classification of documents
Chapter 9: Modeling text data: Topic models
Chapter 10: N-grams and other ways of analyzing adjacent words
Chapter 11: Concluding remarks
The authors balance sophisticated analysis in R with the fundamentals of text mining so that all readers can understand and apply to their own analysis of text data.
University of North Texas
If you have a little experience with R, Ledolter and Vandervelde have created an accessible book for learning to analyze text. They provide a scaffolded experience with concrete examples and access to the text and code. They also provide technical information for those interested in a deeper dive of the material. Readers will feel comfortable analyzing their own text as they use the provided material and progress through the book. I will be adding this book to my applied practicum course.
Duquesne University
