This workbook provides a brief introduction to digital text analysis through a series of three-part units. Each unit introduces students to a concept, a tool for or method of digital text analysis, and a series of exercises for practicing the new skills. In some cases, studies of particular projects are presented instead of tools in the third section of each unit.
The materials here are meant to form the basis for a digital text analysis course that does not require extensive training in programming and is written with student readers in mind. Originally developed for use in a course titled “Scandal, Crime, and Spectacle in the Nineteenth Century,” this workbook draws from these course materials for its datasets and prompts. The book is intended to be modularized enough that it could be used in conjunction with other courses either in whole or in part, as all of its materials are openly available on GitHub. The tripartite structure of each chapter means that sections can be easily removed and replaced with different tools or content. In particular, we envision our course-specific exercises in the third section of each chapter to be removable.
Author(s): Brandon Walsh; Sarah Horowitz
Year: 2017
Language: English
Pages: 161
Preface
Acknowledgements
Introduction
For Instructors
For Students
Schedule
Issues in Digital Text Analysis
Why Read with a Computer?
Google NGram Viewer
Exercises
Close Reading
Close Reading and Sources
Prism Part One
Exercises
Crowdsourcing
Crowdsourcing
Prism Part Two
Exercises
Digital Archives
Text Encoding Initiative
NINES and Digital Archives
Exercises
Data Cleaning
Problems with Data
Zotero
Exercises
Cyborg Readers
How Computers Read Texts
Voyant Part One
Exercises
Reading at Scale
Distant Reading
Voyant Part Two
Exercises
Topic Modeling
Bags of Words
Topic Modeling Case Study
Exercises
Classifiers
Supervised Classifiers
Classifying Texts
Exercises
Sentiment Analysis
Sentiment Analysis
Sentiment Analysis in Action
Exercises
Conclusion
Where to Go Next
Further Resources
Adapting This Book