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Produktbild: Text as Data

Text as Data A New Framework for Machine Learning and the Social Sciences

57,99 €

inkl. gesetzl. MwSt., Versandkostenfrei

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

29.03.2022

Verlag

University Presses

Seitenzahl

360

Maße (L/B/H)

25,4/17,8/1,9 cm

Gewicht

703 g

Sprache

Englisch

ISBN

978-0-691-20755-1

Beschreibung

Produktdetails

Einband

Taschenbuch

Erscheinungsdatum

29.03.2022

Verlag

University Presses

Seitenzahl

360

Maße (L/B/H)

25,4/17,8/1,9 cm

Gewicht

703 g

Sprache

Englisch

ISBN

978-0-691-20755-1

Herstelleradresse

Libri GmbH
Europaallee 1
36244 Bad Hersfeld
DE

Email: GPSR Kontakt

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  • Produktbild: Text as Data

    • Preface
      • Prerequisites and Notation
      • Uses for This Book
      • What This Book Is Not
    • PART I PRELIMINARIES
      • CHAPTER 1 Introduction
        • 1.1 How This Book Informs the Social Sciences
        • 1.2 How This Book Informs the Digital Humanities
        • 1.3 How This Book Informs Data Science in Industry and Government
        • 1.4 A Guide to This Book
        • 1.5 Conclusion
      • CHAPTER 2 Social Science Research and Text Analysis
        • 2.1 Discovery
        • 2.2 Measurement
        • 2.3 Inference
        • 2.4 Social Science as an Iterative and Cumulative Process
        • 2.5 An Agnostic Approach to Text Analysis
        • 2.6 Discovery, Measurement, and Causal Inference: How the Chinese Government Censors Social Media
        • 2.7 Six Principles of Text Analysis
          • 2.7.1 Social Science Theories and Substantive Knowledge are Essential for Research Design
          • 2.7.2 Text Analysis does not Replace Humans—It Augments Them
          • 2.7.3 Building, Refining, and Testing Social Science Theories Requires Iteration and Cumulation
          • 2.7.4 Text Analysis Methods Distill Generalizations from Language
          • 2.7.5 The Best Method Depends on the Task
          • 2.7.6 Validations are Essential and Depend on the Theory and the Task
        • 2.8 Conclusion: Text Data and Social Science
        • PART II SELECTION AND REPRESENTATION
          • CHAPTER 3 Principles of Selection and Representation
            • 3.1 Principle 1: Question-Specific Corpus Construction
            • 3.2 Principle 2: No Values-Free Corpus Construction
            • 3.3 Principle 3: No Right Way to Represent Text
            • 3.4 Principle 4: Validation
            • 3.5 State of the Union Addresses
            • 3.6 The Authorship of the Federalist Papers
            • 3.7 Conclusion
          • CHAPTER 4 Selecting Documents
            • 4.1 Populations and Quantities of Interest
            • 4.2 Four Types of Bias
              • 4.2.1 Resource Bias
              • 4.2.2 Incentive Bias
              • 4.2.3 Medium Bias
              • 4.2.4 Retrieval Bias
            • 4.3 Considerations of “Found Data”
            • 4.4 Conclusion
            • CHAPTER 5 Bag of Words
              • 5.1 The Bag of Words Model
              • 5.2 Choose the Unit of Analysis
              • 5.3 Tokenize
              • 5.4 Reduce Complexity
                • 5.4.1 Lowercase
                • 5.4.2 Remove Punctuation
                • 5.4.3 Remove Stop Words
                • 5.4.4 Create Equivalence Classes (Lemmatize/Stem)
                • 5.4.5 Filter by Frequency
              • 5.5 Construct Document-Feature Matrix
              • 5.6 Rethinking the Defaults
                • 5.6.1 Authorship of the Federalist Papers
                • 5.6.2 The Scale Argument against Preprocessing
              • 5.7 Conclusion
              • CHAPTER 6 The Multinomial Language Model
                • 6.1 Multinomial Distribution
                • 6.2 Basic Language Modeling
                • 6.3 Regularization and Smoothing
                • 6.4 The Dirichlet Distribution
                • 6.5 Conclusion
              • CHAPTER 7 The Vector Space Model and Similarity Metrics
                • 7.1 Similarity Metrics
                • 7.2 Distance Metrics
                • 7.3 tf-idf Weighting
                • 7.4 Conclusion
              • CHAPTER 8 Distributed Representations of Words
                • 8.1 Why Word Embeddings
                • 8.2 Estimating Word Embeddings
                  • 8.2.1 The Self-Supervision Insight
                  • 8.2.2 Design Choices in Word Embeddings
                  • 8.2.3 Latent Semantic Analysis
                  • 8.2.4 Neural Word Embeddings
                  • 8.2.5 Pretrained Embeddings
                  • 8.2.6 Rare Words
                  • 8.2.7 An Illustration
                • 8.3 Aggregating Word Embeddings to the Document Level
                • 8.4 Validation
                • 8.5 Contextualized Word Embeddings
                • 8.6 Conclusion
                • CHAPTER 9 Representations from Language Sequences
                  • 9.1 Text Reuse
                  • 9.2 Parts of Speech Tagging
                    • 9.2.1 Using Phrases to Improve Visualization
                  • 9.3 Named-Entity Recognition
                  • 9.4 Dependency Parsing
                  • 9.5 Broader Information Extraction Tasks
                  • 9.6 Conclusion
                  • PART III DISCOVERY
                    • CHAPTER 10 Principles of Discovery
                      • 10.1 Principle 1: Context Relevance
                      • 10.2 Principle 2: No Ground Truth
                      • 10.3 Principle 3: Judge the Concept, Not the Method
                      • 10.4 Principle 4: Separate Data Is Best
                      • 10.5 Conceptualizing the US Congress
                      • 10.6 Conclusion
                    • CHAPTER 11 Discriminating Words
                      • 11.1 Mutual Information
                      • 11.2 Fightin’ Words
                      • 11.3 Fictitious Prediction Problems
                        • 11.3.1 Standardized Test Statistics as Measures of Separation
                        • 11.3.2 ¿2 Test Statistics
                        • 11.3.3 Multinomial Inverse Regression
                      • 11.4 Conclusion
                      • CHAPTER 12 Clustering
                        • 12.1 An Initial Example Using k-Means Clustering
                        • 12.2 Representations for Clustering
                        • 12.3 Approaches to Clustering
                          • 12.3.1 Components of a Clustering Method
                          • 12.3.2 Styles of Clustering Methods
                          • 12.3.3 Probabilistic Clustering Models
                          • 12.3.4 Algorithmic Clustering Models
                          • 12.3.5 Connections between Probabilistic and Algorithmic Clustering
                        • 12.4 Making Choices
                          • 12.4.1 Model Selection
                          • 12.4.2 Careful Reading
                          • 12.4.3 Choosing the Number of Clusters
                        • 12.5 The Human Side of Clustering
                          • 12.5.1 Interpretation
                          • 12.5.2 Interactive Clustering
                        • 12.6 Conclusion
                        • CHAPTER 13 Topic Models
                          • 13.1 Latent Dirichlet Allocation
                            • 13.1.1 Inference
                            • 13.1.2 Example: Discovering Credit Claiming for Fire Grants in Congressional Press Releases
                          • 13.2 Interpreting the Output of Topic Models
                          • 13.3 Incorporating Structure into LDA
                            • 13.3.1 Structure with Upstream, Known Prevalence Covariates
                            • 13.3.2 Structure with Upstream, Known Content Covariates
                            • 13.3.3 Structure with Downstream, Known Covariates
                            • 13.3.4 Additional Sources of Structure
                          • 13.4 Structural Topic Models
                            • 13.4.1 Example: Discovering the Components of Radical Discourse
                          • 13.5 Labeling Topic Models
                          • 13.6 Conclusion
                          • CHAPTER 14 Low-Dimensional Document Embeddings
                            • 14.1 Principal Component Analysis
                              • 14.1.1 Automated Methods for Labeling Principal Components
                              • 14.1.2 Manual Methods for Labeling Principal Components
                              • 14.1.3 Principal Component Analysis of Senate Press Releases
                              • 14.1.4 Choosing the Number of Principal Components
                            • 14.2 Classical Multidimensional Scaling
                              • 14.2.1 Extensions of Classical MDS
                              • 14.2.2 Applying Classical MDS to Senate Press Releases
                            • 14.3 Conclusion
                            • PART IV MEASUREMENT
                              • CHAPTER 15 Principles of Measurement
                                • 15.1 From Concept to Measurement
                                • 15.2 What Makes a Good Measurement
                                  • 15.2.1 Principle 1: Measures should have Clear Goals
                                  • 15.2.2 Principle 2: Source Material should Always be Identified and Ideally Made Public
                                  • 15.2.3 Principle 3: The Coding Process should be Explainable and Reproducible
                                  • 15.2.4 Principle 4: The Measure should be Validated
                                  • 15.2.5 Principle 5: Limitations should be Explored, Documented and Communicated to the Audience
                                • 15.3 Balancing Discovery and Measurement with Sample Splits
                                • CHAPTER 16 Word Counting
                                  • 16.1 Keyword Counting
                                  • 16.2 Dictionary Methods
                                  • 16.3 Limitations and Validations of Dictionary Methods
                                    • 16.3.1 Moving Beyond Dictionaries: Wordscores
                                  • 16.4 Conclusion
                                  • CHAPTER 17 An Overview of Supervised Classification
                                    • 17.1 Example: Discursive Governance
                                    • 17.2 Create a Training Set
                                    • 17.3 Classify Documents with Supervised Learning
                                    • 17.4 Check Performance
                                    • 17.5 Using the Measure
                                    • 17.6 Conclusion
                                  • CHAPTER 18 Coding a Training Set
                                    • 18.1 Characteristics of a Good Training Set
                                    • 18.2 Hand Coding
                                      • 18.2.1 1: Decide on a Codebook
                                      • 18.2.2 2: Select Coders
                                      • 18.2.3 3: Select Documents to Code
                                      • 18.2.4 4: Manage Coders
                                      • 18.2.5 5: Check Reliability
                                      • 18.2.6 Managing Drift
                                      • 18.2.7 Example: Making the News
                                    • 18.3 Crowdsourcing
                                    • 18.4 Supervision with Found Data
                                    • 18.5 Conclusion
                                    • CHAPTER 19 Classifying Documents with Supervised Learning
                                      • 19.1 Naive Bayes
                                        • 19.1.1 The Assumptions in Naive Bayes are Almost Certainly Wrong
                                        • 19.1.2 Naive Bayes is a Generative Model
                                        • 19.1.3 Naive Bayes is a Linear Classifier
                                      • 19.2 Machine Learning
                                        • 19.2.1 Fixed Basis Functions
                                        • 19.2.2 Adaptive Basis Functions
                                        • 19.2.3 Quantification
                                        • 19.2.4 Concluding Thoughts on Supervised Learning with Random Samples
                                      • 19.3 Example: Estimating Jihad Scores
                                      • 19.4 Conclusion
                                      • CHAPTER 20 Checking Performance
                                        • 20.1 Validation with Gold-Standard Data
                                          • 20.1.1 Validation Set
                                          • 20.1.2 Cross-Validation
                                          • 20.1.3 The Importance of Gold-Standard Data
                                          • 20.1.4 Ongoing Evaluations
                                        • 20.2 Validation without Gold-Standard Data
                                          • 20.2.1 Surrogate Labels
                                          • 20.2.2 Partial Category Replication
                                          • 20.2.3 Nonexpert Human Evaluation
                                          • 20.2.4 Correspondence to External Information
                                        • 20.3 Example: Validating Jihad Scores
                                        • 20.4 Conclusion
                                        • CHAPTER 21 Repurposing Discovery Methods
                                          • 21.1 Unsupervised Methods Tend to Measure Subject Better than Subtleties
                                          • 21.2 Example: Scaling via Differential Word Rates
                                          • 21.3 A Workflow for Repurposing Unsupervised Methods for Measurement
                                            • 21.3.1 1: Split the Data
                                            • 21.3.2 2: Fit the Model
                                            • 21.3.3 3: Validate the Model
                                            • 21.3.4 4: Fit to the Test Data and Revalidate
                                          • 21.4 Concerns in Repurposing Unsupervised Methods for Measurement
                                            • 21.4.1 Concern 1: The Method Always Returns a Result
                                            • 21.4.2 Concern 2: Opaque Differences in Estimation Strategies
                                            • 21.4.3 Concern 3: Sensitivity to Unintuitive Hyperparameters
                                            • 21.4.4 Concern 4: Instability in results
                                            • 21.4.5 Rethinking Stability
                                          • 21.5 Conclusion
                                          • PART V INFERENCE
                                            • CHAPTER 22 Principles of Inference
                                              • 22.1 Prediction
                                              • 22.2 Causal Inference
                                                • 22.2.1 Causal Inference Places Identification First
                                                • 22.2.2 Prediction Is about Outcomes That Will Happen, Causal Inference is about Outcomes from Interventions
                                                • 22.2.3 Prediction and Causal Inference Require Different Validations
                                                • 22.2.4 Prediction and Causal Inference Use Features Differently
                                              • 22.3 Comparing Prediction and Causal Inference
                                              • 22.4 Partial and General Equilibrium in Prediction and Causal Inference
                                              • 22.5 Conclusion
                                              • CHAPTER 23 Prediction
                                                • 23.1 The Basic Task of Prediction
                                                • 23.2 Similarities and Differences between Prediction and Measurement
                                                • 23.3 Five Principles of Prediction
                                                  • 23.3.1 Predictive Features do not have to Cause the Outcome
                                                  • 23.3.2 Cross-Validation is not Always a Good Measure of Predictive Power
                                                  • 23.3.3 It’s Not Always Better to be More Accurate on Average
                                                  • 23.3.4 There can be Practical Value in Interpreting Models for Prediction
                                                  • 23.3.5 It can be Difficult to Apply Prediction to Policymaking
                                                • 23.4 Using Text as Data for Prediction: Examples
                                                  • 23.4.1 Source Prediction
                                                  • 23.4.2 Linguistic Prediction
                                                  • 23.4.3 Social Forecasting
                                                  • 23.4.4 Nowcasting
                                                • 23.5 Conclusion
                                                • CHAPTER 24 Causal Inference
                                                  • 24.1 Introduction to Causal Inference
                                                  • 24.2 Similarities and Differences between Prediction and Measurement, and Causal Inference
                                                  • 24.3 Key Principles of Causal Inference with Text
                                                    • 24.3.1 The Core Problems of Causal Inference Remain, even when Working with Text
                                                    • 24.3.2 Our Conceptualization of the Treatment and Outcome Remains a Critical Component of Causal Inference with Text
                                                    • 24.3.3 The Challenges of Making Causal Inferences with Text Underscore the Need for Sequential Science
                                                  • 24.4 The Mapping Function
                                                    • 24.4.1 Causal Inference with g
                                                    • 24.4.2 Identification and Overfitting
                                                  • 24.5 Workflows for Making Causal Inferences with Text
                                                    • 24.5.1 Define g before Looking at the Documents
                                                    • 24.5.2 Use a Train/Test Split
                                                    • 24.5.3 Run Sequential Experiments
                                                  • 24.6 Conclusion
                                                  • CHAPTER 25 Text as Outcome
                                                    • 25.1 An Experiment on Immigration
                                                    • 25.2 The Effect of Presidential Public Appeals
                                                    • 25.3 Conclusion
                                                  • CHAPTER 26 Text as Treatment
                                                    • 26.1 An Experiment Using Trump’s Tweets
                                                    • 26.2 A Candidate Biography Experiment
                                                    • 26.3 Conclusion
                                                  • CHAPTER 27 Text as Confounder
                                                    • 27.1 Regression Adjustments for Text Confounders
                                                    • 27.2 Matching Adjustments for Text
                                                    • 27.3 Conclusion
                                                  • PART VI CONCLUSION
                                                    • CHAPTER 28 Conclusion
                                                      • 28.1 How to Use Text as Data in the Social Sciences
                                                        • 28.1.1 The Focus on Social Science Tasks
                                                        • 28.1.2 Iterative and Sequential Nature of the Social Sciences
                                                        • 28.1.3 Model Skepticism and the Application of Machine Learning to the Social Sciences
                                                      • 28.2 Applying Our Principles beyond Text Data
                                                      • 28.3 Avoiding the Cycle of Creation and Destruction in Social Science Methodology
                                                      • Acknowledgments
                                                      • Bibliography
                                                      • Index