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Text analysis in Python for social scientists = prediction and classification /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Text analysis in Python for social scientists/ Dirk Hovy.
Reminder of title:	prediction and classification /
Author:	Hovy, Dirk.
Published:	Cambridge :Cambridge University Press, : 2022.,
Description:	92 p. :ill., digital ;23 cm.
Notes:	Title from publisher's bibliographic system (viewed on 21 Feb 2022).
Subject:	Text data mining. -
Online resource:	https://doi.org/10.1017/9781108960885
ISBN:	9781108960885

Text analysis in Python for social scientists = prediction and classification /
Hovy, Dirk.

Text analysis in Python for social scientistsprediction and classification /[electronic resource] :Dirk Hovy. - Cambridge :Cambridge University Press,2022. - 92 p. :ill., digital ;23 cm. - Cambridge elements. Elements in quantitative and computational methods for the social sciences,2398-4023. - Cambridge elements.Elements in quantitative and computational methods for the social sciences.

Title from publisher's bibliographic system (viewed on 21 Feb 2022).

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.

ISBN: 9781108960885Subjects--Topical Terms:

3495453
Text data mining.

LC Class. No.: QA76.9.D343 / H68 2022

Dewey Class. No.: 006.312

Text analysis in Python for social scientists = prediction and classification /
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520 $a 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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