國立虎尾科技大學 |

Text analysis in Python for social scientists : = prediction and classification /

Record Type:	Language materials, printed : Monograph/item
Title/Author:	Text analysis in Python for social scientists :/ Dirk Hovy.
Reminder of title:	prediction and classification /
Author:	Hovy, Dirk,
Description:	1 online resource (92 pages) :digital, PDF file(s). :
Notes:	Title from publisher's bibliographic system (viewed on 21 Feb 2022).
Subject:	Text data mining. -
Online resource:	https://doi.org/10.1017/9781108960885
ISBN:	9781108960885 (ebook)

Text analysis in Python for social scientists : = prediction and classification /
Hovy, Dirk,

Text analysis in Python for social scientists :prediction and classification /Dirk Hovy. - 1 online resource (92 pages) :digital, PDF file(s). - Cambridge elements. Elements in quantitative and computational methods for the social sciences2398-4023.

Title from publisher's bibliographic system (viewed on 21 Feb 2022).

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.

ISBN: 9781108960885 (ebook)Subjects--Topical Terms:

1441399
Text data mining.

LC Class. No.: QA76.9.D343 / H68 2022

Dewey Class. No.: 006.312

Text analysis in Python for social scientists : = prediction and classification /
LDR:02114nam a2200289 i 4500 001 1124079
003 UkCbUP
005 20220228155348.0
006 m|||||o||d||||||||
007 cr||||||||||||
008 240926s2022||||enk o ||1 0|eng|d
020 $a 9781108960885 (ebook)
020 $z 9781108958509 (paperback)
035 $a CR9781108960885
040 $a UkCbUP $b eng $e rda $c UkCbUP
050 4 $a QA76.9.D343 $b H68 2022
082 0 4 $a 006.312 $2 23
100 1 $a Hovy, Dirk, $e author. $3 1441398
245 1 0 $a Text analysis in Python for social scientists : $b prediction and classification / $c Dirk Hovy.
264 1 $a Cambridge : $b Cambridge University Press, $c 2022.
300 $a 1 online resource (92 pages) : $b digital, PDF file(s).
336 $a text $b txt $2 rdacontent
337 $a computer $b c $2 rdamedia
338 $a online resource $b cr $2 rdacarrier
490 0 $a Cambridge elements. Elements in quantitative and computational methods for the social sciences $x 2398-4023
500 $a Title from publisher's bibliographic system (viewed on 21 Feb 2022).
520 $a 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.
650 0 $a Text data mining. $3 1441399
650 0 $a Social sciences $x Computer programs. $3 1406634
650 0 $a Social sciences $x Data processing. $3 555668
650 0 $a Python (Computer program language) $3 566246
776 0 8 $i Print version: $z 9781108958509
856 4 0 $u https://doi.org/10.1017/9781108960885