Text Analysis in Python for Social Scientists: Prediction and Classification - Brossura
Libro 11 di 16: Elements in Quantitative and Computational Methods for the Social Sciences
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A practical guide to text classification and neural networks in Python for social scientists.
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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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Cambridge University Press, 2022
ISBN 10: 1108958508
ISBN 13: 9781108958509
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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 or Softback)
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Text Analysis in Python for Social Scientists : Prediction and Classification
Editore:
Cambridge University Press, 2022
ISBN 10: 1108958508
ISBN 13: 9781108958509
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Text Analysis in Python for Social Scientists (Paperback)
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Cambridge University Press, Cambridge, 2022
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ISBN 13: 9781108958509
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Paperback. Condizione: 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. Codice articolo 9781108958509
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Text Analysis in Python for Social Scientists
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Cambridge University Press, GB, 2022
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ISBN 13: 9781108958509
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Paperback. Condizione: New. 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. Codice articolo LU-9781108958509
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Text Analysis in Python for Social Scientists: Prediction and Classification
Editore:
Cambridge University Press, 2022
ISBN 10: 1108958508
ISBN 13: 9781108958509
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Paperback. Condizione: Brand New. 2nd edition. 75 pages. 9.00x6.00x0.21 inches. In Stock. This item is printed on demand. Codice articolo __1108958508
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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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Condizione: New. New edition NO-PA16APR2015-KAP. Codice articolo 26389700308
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