Idioma: Inglés
Publicado por Cambridge University Press, 2022
ISBN 10: 1108958508 ISBN 13: 9781108958509
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Idioma: Inglés
Publicado por Cambridge University Press, 2022
ISBN 10: 1108958508 ISBN 13: 9781108958509
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Idioma: Inglés
Publicado por Cambridge University Press 3/17/2022, 2022
ISBN 10: 1108958508 ISBN 13: 9781108958509
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EUR 26,97
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Añadir al carritoPaperback or Softback. Condición: New. Text Analysis in Python for Social Scientists. Book.
Idioma: Inglés
Publicado por Cambridge University Press, 2022
ISBN 10: 1108958508 ISBN 13: 9781108958509
Librería: California Books, Miami, FL, Estados Unidos de America
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Idioma: Inglés
Publicado por Cambridge University Press, 2022
ISBN 10: 1108958508 ISBN 13: 9781108958509
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Idioma: Inglés
Publicado por Cambridge University Press, GB, 2022
ISBN 10: 1108958508 ISBN 13: 9781108958509
Librería: Rarewaves.com USA, London, LONDO, Reino Unido
EUR 28,23
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Añadir al carritoPaperback. Condición: 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.
Idioma: Inglés
Publicado por Cambridge University Press, 2022
ISBN 10: 1108958508 ISBN 13: 9781108958509
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Idioma: Inglés
Publicado por Cambridge University Press, 2022
ISBN 10: 1108958508 ISBN 13: 9781108958509
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EUR 37,34
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Añadir al carritoCondición: New. New edition NO-PA16APR2015-KAP.
Idioma: Inglés
Publicado por Cambridge University Press 2022-03, 2022
ISBN 10: 1108958508 ISBN 13: 9781108958509
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Idioma: Inglés
Publicado por Cambridge University Press, 2022
ISBN 10: 1108958508 ISBN 13: 9781108958509
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Idioma: Inglés
Publicado por Cambridge University Press, 2022
ISBN 10: 1108958508 ISBN 13: 9781108958509
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Añadir al carritoPaperback. Condición: Brand New. 2nd edition. 75 pages. 9.00x6.00x0.21 inches. In Stock.
Idioma: Inglés
Publicado por Cambridge University Press, 2022
ISBN 10: 1108958508 ISBN 13: 9781108958509
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Idioma: Inglés
Publicado por Cambridge University Press, 2022
ISBN 10: 1108958508 ISBN 13: 9781108958509
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Idioma: Inglés
Publicado por Cambridge University Press, 2022
ISBN 10: 1108958508 ISBN 13: 9781108958509
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Idioma: Inglés
Publicado por Cambridge University Press, GB, 2022
ISBN 10: 1108958508 ISBN 13: 9781108958509
Librería: Rarewaves.com UK, London, Reino Unido
EUR 25,48
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Añadir al carritoPaperback. Condición: 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.
Idioma: Inglés
Publicado por Cambridge University Press, Cambridge, 2022
ISBN 10: 1108958508 ISBN 13: 9781108958509
Librería: Grand Eagle Retail, Bensenville, IL, Estados Unidos de America
EUR 30,75
Cantidad disponible: 1 disponibles
Añadir al carritoPaperback. Condición: 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 multiple locations in the US or from the UK, depending on stock availability.
Idioma: Inglés
Publicado por Cambridge University Press, 2022
ISBN 10: 1108958508 ISBN 13: 9781108958509
Librería: Revaluation Books, Exeter, Reino Unido
EUR 20,93
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Añadir al carritoPaperback. Condición: Brand New. 2nd edition. 75 pages. 9.00x6.00x0.21 inches. In Stock. This item is printed on demand.
Idioma: Inglés
Publicado por Cambridge University Press, 2022
ISBN 10: 1108958508 ISBN 13: 9781108958509
Librería: THE SAINT BOOKSTORE, Southport, Reino Unido
EUR 26,24
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Idioma: Inglés
Publicado por Cambridge University Press, 2022
ISBN 10: 1108958508 ISBN 13: 9781108958509
Librería: Majestic Books, Hounslow, Reino Unido
EUR 34,63
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Idioma: Inglés
Publicado por Cambridge University Press, 2022
ISBN 10: 1108958508 ISBN 13: 9781108958509
Librería: Biblios, Frankfurt am main, HESSE, Alemania
EUR 34,85
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Idioma: Inglés
Publicado por Cambridge University Press, Cambridge, 2022
ISBN 10: 1108958508 ISBN 13: 9781108958509
Librería: CitiRetail, Stevenage, Reino Unido
EUR 31,61
Cantidad disponible: 1 disponibles
Añadir al carritoPaperback. Condición: 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.
Librería: moluna, Greven, Alemania
EUR 29,40
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Añadir al carritoKartoniert / Broschiert. Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. 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 .