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Natural Language Politics /

Burnham, Michael,

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Natural Language Politics /

Record Type:	Electronic resources : Monograph/item
Title/Author:	Natural Language Politics // Michael Burnham.
Author:	Burnham, Michael,
Description:	1 electronic resource (131 pages)
Notes:	Source: Dissertations Abstracts International, Volume: 86-05, Section: B.
Contained By:	Dissertations Abstracts International86-05B.
Subject:	Text categorization. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31631266
ISBN:	9798346387428

Natural Language Politics /
Burnham, Michael,

Natural Language Politics /Michael Burnham. - 1 electronic resource (131 pages)

Source: Dissertations Abstracts International, Volume: 86-05, Section: B.

This dissertation explores the how recent advancements in text analysis methods can be applied to political research. In the first chapter, I demonstrate how language models can facilitate opinion mining at a much larger scale than was previously possible, and guide researchers through best practices in employing these methods. In the second chapter, I build on these methods by presenting a method of estimating ideology from text that is based on expressed opinions rather than the choice of words. Finally, in chapter three I demonstrate how these methods enable researchers to ask and answer questions about politics that may have been infeasible previously. The chapter uses text analysis to present the first scaling method for affective polarization among members of congress, and then uses this measure to test how affective polarization influences legislative effectiveness.

English

ISBN: 9798346387428Subjects--Topical Terms:

3689475
Text categorization.

Natural Language Politics /
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520 $a This dissertation explores the how recent advancements in text analysis methods can be applied to political research. In the first chapter, I demonstrate how language models can facilitate opinion mining at a much larger scale than was previously possible, and guide researchers through best practices in employing these methods. In the second chapter, I build on these methods by presenting a method of estimating ideology from text that is based on expressed opinions rather than the choice of words. Finally, in chapter three I demonstrate how these methods enable researchers to ask and answer questions about politics that may have been infeasible previously. The chapter uses text analysis to present the first scaling method for affective polarization among members of congress, and then uses this measure to test how affective polarization influences legislative effectiveness.
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