Screen LibraryText analysis in Python for social scientists : prediction and classification /
Text contains a wealth of information 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 the text:...
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| Main Authors: | |
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| Published: |
Cambridge University Press,
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| Publisher Address: | Cambridge, UK : |
| Publication Dates: | 2022. |
| Literature type: | Book |
| Language: | English |
| Series: |
Cambridge elements. Elements in quantitative and computational methods for the social sciences,
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| Subjects: | |
| Summary: |
Text contains a wealth of information 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 the text: power, trust, and 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--back cover. |
| Carrier Form: | 92 pages : color illustrations ; 23 cm. |
| Bibliography: | Includes bibliographical references (pages [83]-92). |
| ISBN: |
9781108958509 1108958508 |
| Index Number: | QA76 |
| CLC: | C91-37 |
| Call Number: | C91-37/H846 |
| 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 -- References. |