| Description |
1 online resource (92 pages) : color illustrations |
| Series |
Cambridge elements. Elements in quantitative and computational methods for the social sciences, 2398-4023 |
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Elements in quantitative and computational methods for social science.
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| Contents |
Introduction -- Background: classification and predication -- 1. Ethics, fairness, and bias -- Prediction: using patterns in the data -- 2. Classification -- 3. Text as input -- 4. Labels -- 5. Train-dev-test -- 6. Performance metrics -- 7. Comparison and significance testing -- 8. Overfitting and regularization -- 9. Model selection and other classifiers -- 10. Model bias -- 11. Feature selection -- 12. Structured prediction -- Neural networks -- 13. Background of neural networks -- 14. Neural architectures and models |
| Summary |
Text contains a wealth of information about about a wide variety of sociocultural constructs. Automated prediction methods can infer these quantities (sentiment analysis is probably the most well-known application). However, there is virtually no limit to the kinds of things we can predict from text: power, trust, misogyny, are all signaled in language. These algorithms easily scale to corpus sizes infeasible for manual analysis. Prediction algorithms have become steadily more powerful, especially with the advent of neural network methods. However, applying these techniques usually requires profound programming knowledge and machine learning expertise. As a result, many social scientists do not apply them. This Element provides the working social scientist with an overview of the most common methods for text classification, an intuition of their applicability, and Python code to execute them. It covers both the ethical foundations of such work as well as the emerging potential of neural network methods.-- Provided by publisher |
| Bibliography |
Includes bibliographical references |
| Notes |
Dirk Hovy is an Associate Professor of computer science in the Marketing Department of Università commerciale Luigi Bocconi, Milan, Italy |
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Print version record |
| Subject |
Text data mining.
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Social sciences -- Computer programs.
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Social sciences -- Data processing.
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Python (Computer program language)
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Python (Computer program language)
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Social sciences -- Computer programs
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Social sciences -- Data processing
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Text data mining
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| Form |
Electronic book
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| ISBN |
9781108960885 |
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110896088X |
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