Justin Grimmer: Text as Data | Taschenbuch

Cover: 9780691207551 | Text as Data | Justin Grimmer (u. a.) | Taschenbuch | Englisch | 2022

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Beschreibung

A guide for using computational text analysis to learn about the social world

From social media posts and text messages to digital government documents and archives, researchers are bombarded with a deluge of text reflecting the social world. This textual data gives unprecedented insights into fundamental questions in the social sciences, humanities, and industry. Meanwhile new machine learning tools are rapidly transforming the way science and business are conducted. Text as Data shows how to combine new sources of data, machine learning tools, and social science research design to develop and evaluate new insights.

Text as Data is organized around the core tasks in research projects using text-representation, discovery, measurement, prediction, and causal inference. The authors offer a sequential, iterative, and inductive approach to research design. Each research task is presented complete with real-world applications, example methods, and a distinct style of task-focused research.

Bridging many divides-computer science and social science, the qualitative and the quantitative, and industry and academia-Text as Data is an ideal resource for anyone wanting to analyze large collections of text in an era when data is abundant and computation is cheap, but the enduring challenges of social science remain.

Overview of how to use text as data
Research design for a world of data deluge
Examples from across the social sciences and industry

Über den Autor

Justin Grimmer, Margaret E. Roberts, and Brandon M. Stewart

Inhaltsverzeichnis

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...

Details

Erscheinungsjahr:	2022
Genre:	Importe , Soziologie
Rubrik:	Wissenschaften
Medium:	Taschenbuch
Inhalt:	Einband - flex.(Paperback)
ISBN-13:	9780691207551
ISBN-10:	0691207550
Sprache:	Englisch
Einband:	Kartoniert / Broschiert
Autor:	Grimmer, Justin Roberts, Margaret E. Stewart, Brandon M.
Hersteller:	Princeton University Press
Verantwortliche Person für die EU:	Libri GmbH, Europaallee 1, D-36244 Bad Hersfeld, gpsr@libri.de
Maße:	254 x 178 x 19 mm
Von/Mit:	Justin Grimmer (u. a.)
Erscheinungsdatum:	21.06.2022
Gewicht:	0,677 kg