語系:
繁體中文
English
說明(常見問題)
回圖書館首頁
手機版館藏查詢
登入
回首頁
到查詢結果
[ null ]
切換:
標籤
|
MARC模式
|
ISBD
Natural Language Politics /
~
Burnham, Michael,
FindBook
Google Book
Amazon
博客來
Natural Language Politics /
紀錄類型:
書目-電子資源 : Monograph/item
正題名/作者:
Natural Language Politics // Michael Burnham.
作者:
Burnham, Michael,
面頁冊數:
1 electronic resource (131 pages)
附註:
Source: Dissertations Abstracts International, Volume: 86-05, Section: B.
Contained By:
Dissertations Abstracts International86-05B.
標題:
Text categorization. -
電子資源:
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 /
LDR
:02249nmm a22003973i 4500
001
2431652
005
20250616055549.5
006
m o d
007
cr|nu||||||||
008
261011s2024 miu||||||m |||||||eng d
020
$a
9798346387428
035
$a
(MiAaPQD)AAI31631266
035
$a
(MiAaPQD)PennState19177mlb6496
035
$a
AAI31631266
040
$a
MiAaPQD
$b
eng
$c
MiAaPQD
$e
rda
100
1
$a
Burnham, Michael,
$e
author.
$3
3820566
245
1 0
$a
Natural Language Politics /
$c
Michael Burnham.
264
1
$a
Ann Arbor :
$b
ProQuest Dissertations & Theses,
$c
2024
300
$a
1 electronic resource (131 pages)
336
$a
text
$b
txt
$2
rdacontent
337
$a
computer
$b
c
$2
rdamedia
338
$a
online resource
$b
cr
$2
rdacarrier
500
$a
Source: Dissertations Abstracts International, Volume: 86-05, Section: B.
500
$a
Advisors: Nelson, Michael J. Committee members: Banaszak, Lee Ann; Monroe, Burt; Rajtmajer, Sarah; Linn, Suzanna; Munger, Kevin; Desmarais, Bruce.
502
$b
Ph.D.
$c
The Pennsylvania State University
$d
2024.
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.
546
$a
English
590
$a
School code: 0176
650
4
$a
Text categorization.
$3
3689475
650
4
$a
Nominations.
$3
3564149
650
4
$a
Ideology.
$3
521772
650
4
$a
Sentiment analysis.
$2
lcstt
$3
3266790
650
4
$a
Neural networks.
$3
677449
650
4
$a
Labeling.
$3
3560710
650
4
$a
Political parties.
$3
516328
650
4
$a
Political science.
$3
528916
650
4
$a
Attitudes.
$3
1973410
650
4
$a
Documents.
$3
3815417
650
4
$a
Automatic text analysis.
$3
3686614
650
4
$a
Social sciences.
$3
516032
650
4
$a
Semantics.
$3
520060
650
4
$a
Natural language.
$3
3562052
650
4
$a
COVID-19.
$3
3554449
650
4
$a
Web studies.
$3
2122754
650
4
$a
Logic.
$3
529544
690
$a
0615
690
$a
0800
690
$a
0646
690
$a
0395
710
2
$a
The Pennsylvania State University.
$e
degree granting institution.
$3
3765934
720
1
$a
Nelson, Michael J.
$e
degree supervisor.
773
0
$t
Dissertations Abstracts International
$g
86-05B.
790
$a
0176
791
$a
Ph.D.
792
$a
2024
856
4 0
$u
https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31631266
筆 0 讀者評論
館藏地:
全部
電子資源
出版年:
卷號:
館藏
1 筆 • 頁數 1 •
1
條碼號
典藏地名稱
館藏流通類別
資料類型
索書號
使用類型
借閱狀態
預約狀態
備註欄
附件
W9527347
電子資源
11.線上閱覽_V
電子書
EB
一般使用(Normal)
在架
0
1 筆 • 頁數 1 •
1
多媒體
評論
新增評論
分享你的心得
Export
取書館
處理中
...
變更密碼
登入
(1)帳號:一般為「身分證號」;外籍生或交換生則為「學號」。 (2)密碼:預設為帳號末四碼。
帳號
.
密碼
.
請在此電腦上記得個人資料
取消
忘記密碼? (請注意!您必須已在系統登記E-mail信箱方能使用。)