Text Analysis in Python for Social Scientists (Paperback)

Dirk Hovy

ISBN 10: 1108958508 / ISBN 13: 9781108958509

Published by Cambridge University Press, Cambridge, 2022

About this Item

Paperback. 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. 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. Shipping may be from our Sydney, NSW warehouse or from our UK or US warehouse, depending on stock availability.

Seller Inventory # 9781108958509

Bibliographic Details

• Title: Text Analysis in Python for Social Scientists (Paperback)
• Publisher: Cambridge University Press, Cambridge
• Publication Date: 2022
• Language: English
• ISBN 10: 1108958508
• ISBN 13: 9781108958509
• Binding: Paperback
• Condition: new

About this title

Synopsis

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.