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Analyzing Textual Information Johannes Ledolter

Analyzing Textual Information By Johannes Ledolter

Analyzing Textual Information by Johannes Ledolter

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Analyzing Textual Information Summary

Analyzing Textual Information: From Words to Meanings through Numbers by Johannes Ledolter

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.

Analyzing Textual Information Reviews

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. -- Matthew Eshbaugh-Soha
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. -- James B. Schreiber

About Johannes Ledolter

JOHANNES LEDOLTER has professorships in both the Business School, where he is Robert Thomas Holmes Professor of Business Analytics, and in the Department of Statistics and Actuarial Science at the University of Iowa. He is a Fellow of the American Statistical Association and the American Society for Quality, and Elected Member of the International Statistical Institute. He is the author of several books, including Statistical Methods for Forecasting, Introduction to Regression Modeling, Testing 1-2-3: Experimental Design with Applications in Marketing and Service Operations, and Data Mining and Business Analytics with R. He was Professor of Statistics at the Vienna University of Economics and Business from 1997 to 2015, and held visiting professorships at Princeton, Yale, Stanford and the University of Chicago. Since 2011, he has been Associate Investigator at the Center for Prevention and Treatment of Vision Loss at the Iowa City VA Health Care System, which studies optic nerve and retinal disorders in relation to traumatic brain injury. Professor Ledolter enjoys working on multi-disciplinary projects that involve both numeric and text information. 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.

Table of Contents

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

Additional information

NGR9781544390000
9781544390000
1544390009
Analyzing Textual Information: From Words to Meanings through Numbers by Johannes Ledolter
Johannes Ledolter
Quantitative Applications in the Social Sciences
New
Paperback
SAGE Publications Inc
2021-07-13
192
N/A
Book picture is for illustrative purposes only, actual binding, cover or edition may vary.
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