| 000 -LÍDER |
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| Campo de controle fixo |
nam a22 7a 4500 |
| 003 - CÓDIGO MARC DA AGÊNCIA CATALOGADORA |
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| Campo de controle |
BR-BrENAP |
| 005 - DATA E HORA DA ÚLTIMA ATUALIZAÇÃO |
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| Campo de controle |
20230809180814.0 |
| 008 - CAMPO DE TAMANHO FIXO |
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| Campo fixo de controle |
230314t20222022njua b 001 0 eng d |
| 020 ## - ISBN - INTERNATIONAL STANDARD BOOK NUMBER |
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| ISBN |
9780691207551 |
| 040 ## - FONTE DA CATALOGAÇÃO |
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| Agência catalogadora |
BR-BrENAP |
| Idioma da catalogação |
Pt_BR |
| 041 ## - IDIOMA |
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| Idioma do texto |
eng |
| 090 ## - NÚMERO DE CLASSIFICAÇÃO |
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| Número de Classificação |
006.312 |
| Cutter |
G8648t |
| 100 1# - ENTRADA PRINCIPAL - NOME PESSOAL |
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| Nome pessoal |
Grimmer, Justin |
| 9 (RLIN) |
68335 |
| 245 10 - TÍTULO PRINCIPAL |
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| Título principal |
Text as data : |
| Subtítulo |
a new framework for machine learning and the social sciences / |
| Indicação de responsabilidade |
por Justin Grimmer, Margaret E. Roberts e Brandon M. Stewart. -- |
| 260 ## - IMPRENTA (PUBLICAÇÃO, DISTRIBUIÇÃO, ETC.) |
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| Lugar de publicação, distribuição, etc. |
Nova Jersey, EUA ; |
| -- |
Oxford, UK : |
| Nome do editor, distribuidor, etc. |
Princeton University Press, |
| Data de publicação, distribuição, etc |
2022. |
| 300 ## - DESCRIÇÃO FÍSICA |
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| Extensão |
336 p. : |
| Detalhes físicos adicionais |
il. |
| 505 ## - NOTA DE CONTEÚDO |
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| Título |
Preface |
| -- |
PART I - PRELIMINARIES |
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CHAPTER 1 - Introduction |
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1.1 How This Book Informs the Social Sciences |
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1.2 How This Book Informs the Digital Humanities |
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1.3 How This Book Informs Data Science in Industry and Government |
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1.4 A Guide to This Book |
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1.5 Conclusion |
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CHAPTER 2 - Social Science Research and Text Analysis |
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2.1 Discovery |
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2.2 Measurement |
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2.3 Inference |
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2.4 Social Science as an Interative and Cumulative Process |
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2.5 An Agnostic Approach to Text Analysis |
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2.6 Discovery, Meansurement, and Causal Inference: How the Chinese Government Censors Social Media |
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2.7 Six Principals of Text Analysis |
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2.8 Conclusion: nText Data and Social Science |
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PART II - SELECTION AND REPRESENTATION |
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CHAPTER 3 - Principles of Selection and Representation |
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3.1 Principle 1: Question-Specific Corpus Construction |
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3.2 Principle 2: No Values-Free Corpus Construction |
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3.3 Principle 3: No Right Way to Represent Text |
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3.4 Principle 4: Validation |
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3.5 State of the Union Addresses |
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3.6 The Autorship of the Federalist Papers |
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3.7 Conclusion |
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CHAPTER 4 - Selecting Documents |
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4.1 Populations and Quantities of Interest |
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4.2 Four Types of Bias |
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4.3 Considerations of "Found Data" |
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4.4 Conclusion |
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CHAPTER 5 - Bag of Words |
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5.1 The Bag of Words Model |
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5.2 Choose the Unit of Analysis |
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5.3 Tokenize |
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5.4 Reduce Complexity |
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5.5 Construct Document-Feature Matrix |
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5.6 Rethinking the Defaults |
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5.7 Conclusion |
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CHAPTER 6 - The Multinomial Language Model |
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6.1 Multinomial Distribution |
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6.2 Basic Language Modeling |
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6.3 Regularization and Smoothing |
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6.4 The Dirichlet Distribution |
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6.5 Conclusion |
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CHAPTER 7 - The Vector Space Model and Similarity Metrics |
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7.1 Similarity Metrics |
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7.2 Distance Metrics |
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7.3 tf-idf Weighting |
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7.4 Conclusion |
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CHAPTER 8 - Distributed Representations of Words |
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8.1 Why Word Embeddings |
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8.2 Estimating Word Embeddings |
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8.3 Aggregating Word Embeddings to the Document Level |
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8.4 Validation |
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8.5 Contextualized Word Embeddings |
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8.6 Conclusion |
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CHAPTER 9 - Rpresentations from Language Sequences |
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9.1 Text Reuse |
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9.2 Parts of Speech Tegging |
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9.3 Named-Entity Recognition |
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9.4 Dependency Parsing |
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9.5 Broader Information Extraction Tasks |
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9.6 Conclusion |
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PART III - DISCOVERY |
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CHAPTER 10 - Principles of Discovery |
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10.1 Principle 1: Context Relevance |
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10.2 Principle 2: No Ground Truth |
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10.3 Principle 3: Judge the Concept, Not the Method |
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10.4 Principle 4: Separate Data Is Best |
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10.5 Conceptualizing the US Congress |
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10.6 Conclusion |
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CHAPTER 11 - Discriminating Words |
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11.1 Mutual Information |
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11.2 Fightin' Words |
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11.3 Fictitious Prediction Problems |
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11.4 Conclusion |
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CHAPTER 12 - Clustering |
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12.1 An Initial Example Using k-Means Clustering |
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12.2 Representations to Clustering |
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12.3 Approaches to Clustering |
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12.4 Making Choices |
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12.5 The Human Side of Clustering |
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12.6 Conclusion |
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CHAPTER 13 - Topic Models |
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13.1 Latent Dirichlet Allocation |
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13.2 Interpreting the Output of Topic Models |
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13.3 Incorporating Structure into LDA |
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13.4 Structural Topic Models |
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13.5 Labeling Topic Models |
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13.6 Conclusion |
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CHAPTER 14 - Low-Dimensional Document Embeddings |
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14.1 Principal Component Analysis |
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14.2 Classical Multidimensional Scaling |
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14.3 Conclusion |
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PART IV - MEASUREMENT |
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CHAPTER 15 - Principles of Measurement |
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15.1 From Concept to Measurement |
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15.2 What Makes a Good Measurement |
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15.3 Balancing Discovery and Measurement with Sample Splits |
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CHAPTER 16 - Word Counting |
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16.1 Keyword Counting |
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16.2 Dictionary Methods |
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16.3 Limitations and Validations of Dictionary Methods |
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16.4 Conclusion |
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CHAPTER 17 - An Overview of Supervised Classification |
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17.1 Example: Discursive Governance |
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17.2 Create a Training Set |
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17.3 Classify Documents with Supervised Learning |
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17.4 Check Performance |
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17.5 Using the Measure |
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17.6 Conclusion |
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CHAPTER 18 - Coding a Training Set |
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18.1 Characteristics of a Good Training Set |
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18.2 Hand Coding |
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18.3 Crowdsourcing |
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18.4 Supervision with Found Data |
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18.5 Conclusion |
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CHAPTEER 19 - Classifying Documents with Supervised |
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19.1 Naive Bayes |
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19.2 Machine Learning |
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19.3 Example: Estimating Jihad Scores |
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19.4 Conclusion |
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CHAPTER 20 - Checking Performance |
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20.1 Validation with Gold-Standard Data |
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20.2 Validation without Gold-Standar Data |
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20.3 Example: Validating Jihad Scores |
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20.4 Conclusion |
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CHAPTER 21 - Repurposing Discovery Methods |
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21.1 Unsupervised Methods Tend to Measure Subject |
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21.2 Example: Scaling via Differential Word Rates |
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21.3 A Workflow for Repurposing Unsupervised Methods |
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21.4 Concerns in Repurposing Unsupervised Methods for Measurement |
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21.5 Conclusion |
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PART V - INFERENCE |
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CHAPTER 22 - Principles of inference |
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22.1 Prediction |
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22.2 Causal inference |
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22.3 Comparing Prediction and Causal Inference |
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22.4 Partial and General Equilibrium in Prediction and Causal Inference |
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22.5 Conclusion |
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CHAPTER 23 - Prediction |
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23.1 The Basic Task of Prediction |
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23.2 Similarities and Fifferences between Prediction and Measurement |
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23.3 Five Principles of Prediction |
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23.4 Using Text as Data for Prediction: Examples |
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23.5 Conclusion |
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CHAPTER 24 - Causal Inference |
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24.1 Introduction to Causal Inference |
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24.2 Similarities and Differences between Prediction and Measurement, and Causal Inference |
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24.3 Key Principles of Causal Inference with Text |
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24.4 The Mapping Function |
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24.5 Workflows for Making Causal Inferences with Text |
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24.6 Conclusion |
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CHAPTER 25 - Text as Outcome |
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25.1 An Experiment on Immigration |
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25.2 The Effect of Presidential Public Appeals |
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25.3 Conclusion |
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CHAPTER 26 - Text as Treatment |
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26.1 An Experiment Using Trump's Tweets |
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26.2 A Candidate Biography Experiment |
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26.3 Conclusion |
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CHAPTER 27 - Text as Confounder |
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27.1 Regression Adjustments for Text Confounders |
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27.2 Matching Adjustments for Text |
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27.3 Conclusion |
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PART VI - CONCLUSION |
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28.1 How to Use Text as Data in the Social Sciences |
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28.2 Applying Our Principles beyond Text Data |
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28.3 Avoiding the Cycle of Creation and Destruction in Social Science Methodology |
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Acknowledgments |
| -- |
Bibliography |
| -- |
Index |
| 650 #0 - ENTRADA DE ASSUNTO - ASSUNTO TÓPICO |
|---|
| Cabeçalho tópico ou nome geográfico |
Mineração de Dados de Texto |
| 9 (RLIN) |
68336 |
| 650 #0 - ENTRADA DE ASSUNTO - ASSUNTO TÓPICO |
|---|
| Cabeçalho tópico ou nome geográfico |
Ciências Sociais - Processamento de Dados |
| 9 (RLIN) |
68337 |
| 650 #0 - ENTRADA DE ASSUNTO - ASSUNTO TÓPICO |
|---|
| Cabeçalho tópico ou nome geográfico |
Machine Learning |
| 9 (RLIN) |
68338 |
| 700 1# - ENTRADA SECUNDÁRIA - NOME PESSOAL |
|---|
| Nome pessoal |
Roberts, Margaret E. |
| 9 (RLIN) |
68339 |
| 700 1# - ENTRADA SECUNDÁRIA - NOME PESSOAL |
|---|
| Nome pessoal |
Stewart, Brandon M. |
| 9 (RLIN) |
68340 |
| 909 ## - IDENTIFICAÇÃO DO CATALOGADOR |
|---|
| Ano e mês da catalogação (aaaamm) |
202308 |
| Identificação do catalogador |
Raynara |
| 942 ## - TIPO ESPECÍFICO |
|---|
| Tipo de material |
Livro Geral |
