Johannes Ledolter & Lea S. S. VanderVelde 
Analyzing Textual Information [PDF ebook] 
From Words to Meanings through Numbers

Series Editor’s Introduction
Preface
Acknowledgments
About the Authors
Chapter 1: Introduction
1.1 Text Data
1.2 The Two Applications Considered in This Book
1.3 Introductory Example and Its Analysis Using the R Statistical Software
1.4 The Introductory Example Revisited, Illustrating Concordance and Collocation Using Alternative Software
1.5 Concluding Remarks
1.6 References
Chapter 2: A Description of the Studied Text Corpora and A Discussion of Our Modeling Strategy
2.1 Introduction to the Corpora: Selecting the Texts
2.2 Debates of the 39th U.S. Congress, as recorded in the Congressional Globe
2.3 The Territorial Papers of the United States
2.4 Analyzing Text Data: Bottom-Up or Top-Down Analysis
2.5 References
Appendix to Chapter 2: The Complete Congressional Record
Chapter 3: Preparing Text for Analysis: Text Cleaning and Formatting
3.1 Text Cleaning
3.2 Text Formatting
3.3 Concluding Remarks
3.4 References
Chapter 4: Word Distributions: Document-Term Matrices of Word Frequencies and the “Bag of Words” Representation
4.1 Document-Term Matrices of Frequencies
4.2 Displaying Word Frequencies
4.3 Co-Occurrence of Terms in the Same Document
4.4 The Zipf Law: An Interesting Fact About the Distribution of Word Frequencies
4.5 References
Chapter 5: Metavariables and Text Analysis Stratified on Metavariables
5.1 The Significance of Stratification and the Importance of Metavariables
5.2 Analysis of the Territorial Papers
5.3 Analysis of Speeches From the 39th Congress
5.4 References
Chapter 6: Sentiment Analysis
6.1 Lexicons of Sentiment-Charged Words
6.2 Applying Sentiment Analysis to the Letters of the Territorial Papers
6.3 Using Other Sentiment Dictionaries and the R Software tidytext for Sentiment Analysis
6.4 Concluding Remarks: An Alternative Approach for Sentiment Analysis
6.5 References
Chapter 7: Clustering of Documents
7.1 Clustering Documents
7.2 Measures for the Closeness and the Distance of Documents
7.3 Methods for Clustering Documents
7.4 Illustrating Clustering Methods on a Simulated Example
7.5 References
Chapter 8: Classification of Documents
8.1 Introduction
8.2 Classification Procedures
8.3 Two Examples Using the Congressional Speech Database
8.4 Concluding Remarks on Authorship Attribution: Commenting on the Field of Stylometry
8.5 References
Chapter 9: Modeling Text Data: Topic Models
9.1 Topic Models
9.2 Fitting Topic Models to the Two Corpora Studied in This Book
9.3 References
Chapter 10: n-Grams and Other Ways of Analyzing Adjacent Words
10.1 Analysis of Bigrams
10.2 Text Windows to Measure Word Associations Within a Neighborhood of Words and a Discussion of the R Package text2vec
10.3 Illustrating the Use of n-Grams: Speeches of the 39th Congress
Chapter 11: Concluding Remarks
Appendix: Listing of Website Resources

LEA VANDERVELDE is Josephine Witte Professor of Law at the University of Iowa.  She is an award-winning author in the fields of law and legal history. She is the author of several casebooks, dozens of articles in the nation’s leading law journals, and two historical works, Mrs. Dred Scott and Redemption Songs: Suing for Freedom before Dred Scott.   She has been the Guggenheim Fellow for Constitutional Studies and the May Brodbeck Humanities Fellow, and has held visiting professorships at Yale, the University of Pennsylvania, and the American Bar Foundation.  She is director of the RAOS project, Reconstruction Amendment Optical Scanning, and principle investigator of the Law of the Frontier project at Stanford’s CESTA.  She had given professional lectures all over the world.