Text analysis in python for social scientist:
Hovy, Dirk
Text analysis in python for social scientist: prediction and classification - New York Cambridge University Press 2022 - 92 p.
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.
9781108958509
Python (Computer program language)
Social sciences--Computer programs
Social sciences--Data processing
Text data mining
006.312 / HOV
Text analysis in python for social scientist: prediction and classification - New York Cambridge University Press 2022 - 92 p.
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.
9781108958509
Python (Computer program language)
Social sciences--Computer programs
Social sciences--Data processing
Text data mining
006.312 / HOV