Name: Text Analysis in Python for Social Scientists: Prediction and Classification (Elements in Quantitative and Computational Methods for the Social Sciences) (en Inglés)
SKU: 54046231
Price: 36.92 USD
Availability: InStock
Author: Dirk Hovy
ISBN: 9781108958509

portada Text Analysis in Python for Social Scientists: Prediction and Classification (Elements in Quantitative and Computational Methods for the Social Sciences) (en Inglés)

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

Formato

Libro Físico

Autor

Dirk Hovy

Editorial

Cambridge University Press

Año

2022

Idioma

Inglés

N° páginas

Encuadernación

Tapa Blanda

Dimensiones

22.9x15.2x0.5 cm

Peso

0.14

ISBN13

9781108958509

N° edición

0002

Categorías

Ciencias políticas y teoría

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

Dirk Hovy (Autor) · Cambridge University Press · Tapa Blanda

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Reseña del libro "Text Analysis in Python for Social Scientists: Prediction and Classification (Elements in Quantitative and Computational Methods for the Social Sciences) (en Inglés)"

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.