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Text Analysis in Python for Social Scientists: Prediction and Classification (Elements in Quantitative and Computational Methods for the Social Sciences) - Softcover

 
9781108958509: Text Analysis in Python for Social Scientists: Prediction and Classification (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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ISBN 10: 1108958508 ISBN 13: 9781108958509
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Paperback. Condition: Very Good. Shipped within 24 hours from our UK warehouse. Clean, undamaged book with no damage to pages and minimal wear to the cover. Spine still tight, in very good condition. Remember if you are not happy, you are covered by our 100% money back guarantee. Seller Inventory # 6545-9781108958509

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Dirk Hovy
Published by Cambridge University Press 17 M, 2022
ISBN 10: 1108958508 ISBN 13: 9781108958509
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Paperback. Condition: Very Good. Text Analysis in Python for Social Scientists: Prediction and Classification (Elements in Quantitative and Computational Methods for the Social Sciences) This book is in very good condition and will be shipped within 24 hours of ordering. The cover may have some limited signs of wear but the pages are clean, intact and the spine remains undamaged. This book has clearly been well maintained and looked after thus far. Money back guarantee if you are not satisfied. See all our books here, order more than 1 book and get discounted shipping. . Seller Inventory # 7719-9781108958509

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Paperback. Condition: 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. Seller Inventory # LU-9781108958509

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