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Text Analysis in Python for Social Scientists (Elements in Quantitative and Computational Methods for the Social Sciences) - Softcover
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.
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- PublisherCambridge University Press
- Publication date2022
- ISBN 10 1108958508
- ISBN 13 9781108958509
- BindingPaperback
- LanguageEnglish
- Number of pages102
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Text Analysis in Python for Social Scientists: Prediction and Classification (Elements in Quantitative and Computational Methods for the Social Sciences)
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Text Analysis in Python for Social Scientists: Prediction and Classification (Elements in Quantitative and Computational Methods for the Social Sciences)
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Text Analysis in Python for Social Scientists: Prediction and Classification (Elements in Quantitative and Computational Methods for the Social Sciences)
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Text Analysis in Python for Social Scientists : Prediction and Classification
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Text Analysis in Python for Social Scientists : Prediction and Classification
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Text Analysis in Python for Social Scientists (Paperback)
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Paperback. Condition: new. 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 multiple locations in the US or from the UK, depending on stock availability. Seller Inventory # 9781108958509
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Text Analysis in Python for Social Scientists (Elements in Quantitative and Computational Methods for the Social Sciences)
Published by
Cambridge University Press, 2022
ISBN 10: 1108958508
ISBN 13: 9781108958509
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Text Analysis in Python for Social Scientists (Elements in Quantitative and Computational Methods for the Social Sciences)
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ISBN 10: 1108958508
ISBN 13: 9781108958509
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