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Verlag: Cambridge University Press, 2022
ISBN 10: 1108958508 ISBN 13: 9781108958509
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Sprache: Englisch
Verlag: Cambridge University Press, 2021
ISBN 10: 1108819826 ISBN 13: 9781108819824
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Verlag: Cambridge University Press, 2022
ISBN 10: 1108958508 ISBN 13: 9781108958509
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Paperback or Softback. Zustand: New. Text Analysis in Python for Social Scientists. Book.
Sprache: Englisch
Verlag: Cambridge University Press, 2022
ISBN 10: 1108958508 ISBN 13: 9781108958509
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Sprache: Englisch
Verlag: Cambridge University Press, 2022
ISBN 10: 1108958508 ISBN 13: 9781108958509
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Verlag: Cambridge University Press, 2021
ISBN 10: 1108819826 ISBN 13: 9781108819824
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Sprache: Englisch
Verlag: Cambridge University Press, 2021
ISBN 10: 1108819826 ISBN 13: 9781108819824
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Verlag: Cambridge University Press, GB, 2021
ISBN 10: 1108819826 ISBN 13: 9781108819824
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In den WarenkorbPaperback. Zustand: New. Text is everywhere, and it is a fantastic resource for social scientists. However, because it is so abundant, and because language is so variable, it is often difficult to extract the information we want. There is a whole subfield of AI concerned with text analysis (natural language processing). Many of the basic analysis methods developed are now readily available as Python implementations. This Element will teach you when to use which method, the mathematical background of how it works, and the Python code to implement it.
Sprache: Englisch
Verlag: Cambridge University Press, GB, 2022
ISBN 10: 1108958508 ISBN 13: 9781108958509
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In den WarenkorbPaperback. Zustand: 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.
Sprache: Englisch
Verlag: Cambridge University Press, Cambridge, 2021
ISBN 10: 1108819826 ISBN 13: 9781108819824
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Paperback. Zustand: new. Paperback. Text is everywhere, and it is a fantastic resource for social scientists. However, because it is so abundant, and because language is so variable, it is often difficult to extract the information we want. There is a whole subfield of AI concerned with text analysis (natural language processing). Many of the basic analysis methods developed are now readily available as Python implementations. This Element will teach you when to use which method, the mathematical background of how it works, and the Python code to implement it. Text is a fantastic resource for social scientists, but because it is so abundant, and so variable, it can be difficult to extract the information we want. Many basic text analysis methods are available as Python implementations: this Element will teach you when to use which method, how it works, and the Python code to implement it. Shipping may be from multiple locations in the US or from the UK, depending on stock availability.
Sprache: Englisch
Verlag: Cambridge University Press, Cambridge, 2022
ISBN 10: 1108958508 ISBN 13: 9781108958509
Anbieter: Grand Eagle Retail, Bensenville, IL, USA
Paperback. Zustand: 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.
Sprache: Englisch
Verlag: Cambridge University Press, 2022
ISBN 10: 1108958508 ISBN 13: 9781108958509
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Sprache: Englisch
Verlag: Cambridge University Press, 2021
ISBN 10: 1108819826 ISBN 13: 9781108819824
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ISBN 10: 1108958508 ISBN 13: 9781108958509
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ISBN 10: 1108819826 ISBN 13: 9781108819824
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In den WarenkorbPaperback. Zustand: New. Text is everywhere, and it is a fantastic resource for social scientists. However, because it is so abundant, and because language is so variable, it is often difficult to extract the information we want. There is a whole subfield of AI concerned with text analysis (natural language processing). Many of the basic analysis methods developed are now readily available as Python implementations. This Element will teach you when to use which method, the mathematical background of how it works, and the Python code to implement it.
Sprache: Englisch
Verlag: Cambridge University Press, GB, 2022
ISBN 10: 1108958508 ISBN 13: 9781108958509
Anbieter: Rarewaves.com UK, London, Vereinigtes Königreich
EUR 25,15
Anzahl: Mehr als 20 verfügbar
In den WarenkorbPaperback. Zustand: 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.
Sprache: Englisch
Verlag: Cambridge University Press, 2022
ISBN 10: 1108958508 ISBN 13: 9781108958509
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Verlag: Cambridge University Press, 2021
ISBN 10: 1108819826 ISBN 13: 9781108819824
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ISBN 10: 1108958508 ISBN 13: 9781108958509
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In den WarenkorbPaperback. Zustand: 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. This item is printed on demand. Shipping may be from our UK warehouse or from our Australian or US warehouses, depending on stock availability.