Text analysis in python for social scientist: prediction and classification
Material type: TextPublication details: Cambridge University Press New York 2022Description: 92 pISBN:- 9781108958509
- 006.312 HOV
Item type | Current library | Collection | Call number | Copy number | Status | Date due | Barcode | Item holds | |
---|---|---|---|---|---|---|---|---|---|
Book | Bodh Gaya General Stacks | IT&DS | 006.312 HOV (Browse shelf(Opens below)) | 1 | Available | IIMG-004223 |
Table of Contents 1. Introduction 2. Ethics, Fairness, and Bias 3. Classification 4. Text as Input 5. Labels 6. Train-Dev-Test 7. Performance Metrics 8. Comparison and Significance Testing 9. Overfitting and Regularization 10. Model Selection and Other Classifiers 11. Model Bias 12. Feature Selection 13. Structured Prediction 14. Neural Networks Background 15. Neural Architectures and Models.
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.
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