Analyzing Textual Information: From Words to Meanings through Numbers(Quantitative Applications in the Social Sciences)

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.

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