東華大學圖書館 |

Language: English

Help

回圖書館首頁

手機版館藏查詢

Back

Switch To: Labeled | MARC Mode | ISBD

Combined Word and Network Embeddings...

Singh, Tannu Dharmendra.

Linked to FindBook

Google Book

Amazon

博客來

Combined Word and Network Embeddings: An Analysis Framework of User Opinions on Social Media.

Record Type:	Electronic resources : Monograph/item
Title/Author:	Combined Word and Network Embeddings: An Analysis Framework of User Opinions on Social Media./
Author:	Singh, Tannu Dharmendra.
Published:	Ann Arbor : ProQuest Dissertations & Theses, : 2020,
Description:	56 p.
Notes:	Source: Masters Abstracts International, Volume: 82-02.
Contained By:	Masters Abstracts International82-02.
Subject:	Computer science. -
Online resource:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28086851
ISBN:	9798664718515

Combined Word and Network Embeddings: An Analysis Framework of User Opinions on Social Media.
Singh, Tannu Dharmendra.

Combined Word and Network Embeddings: An Analysis Framework of User Opinions on Social Media. - Ann Arbor : ProQuest Dissertations & Theses, 2020 - 56 p.

Source: Masters Abstracts International, Volume: 82-02.

Thesis (M.S.)--The University of North Carolina at Charlotte, 2020.

This item must not be sold to any third party vendors.

Online social media like Twitter, Facebook, and Gab is often used as the stage to deliver one's opinion for a particular group of people, a political party, etc. Sometimes, the opinions shared are considered as controversial by some audience, applauded by some, or disagreed by some in the form of comments, sharing, likes, or dislikes. The information about shared opinion and the reaction to it in the form of positive, or negative reaction forms an interaction, and a collection of many such interactions forms a signed network. In addition, the evolution of information on social networks strongly relies on the nature of interactions between the users. The study of interactions is, therefore, crucial to predict the extent and nature of information spread. In this work, we study the relationship between users whether they agree or disagree in the dynamic evolution of interactions (cascades) on a larger network, Gab, to predict the relationship between the users on the social network. We quantitatively use the combination of text information and network information to enhance state of the art deep learning models for contradiction detection. The outcome of this research might contribute to improving link prediction.

ISBN: 9798664718515Subjects--Topical Terms:

523869
Computer science.
Subjects--Index Terms:

Combined

Combined Word and Network Embeddings: An Analysis Framework of User Opinions on Social Media.
LDR:02325nmm a2200349 4500 001 2277516
005 20210521102013.5
008 220723s2020 ||||||||||||||||| ||eng d
020 $a 9798664718515
035 $a (MiAaPQ)AAI28086851
035 $a AAI28086851
040 $a MiAaPQ $c MiAaPQ
100 1 $a Singh, Tannu Dharmendra. $3 3555833
245 1 0 $a Combined Word and Network Embeddings: An Analysis Framework of User Opinions on Social Media.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2020
300 $a 56 p.
500 $a Source: Masters Abstracts International, Volume: 82-02.
500 $a Advisor: Krishnan, Siddharth.
502 $a Thesis (M.S.)--The University of North Carolina at Charlotte, 2020.
506 $a This item must not be sold to any third party vendors.
520 $a Online social media like Twitter, Facebook, and Gab is often used as the stage to deliver one's opinion for a particular group of people, a political party, etc. Sometimes, the opinions shared are considered as controversial by some audience, applauded by some, or disagreed by some in the form of comments, sharing, likes, or dislikes. The information about shared opinion and the reaction to it in the form of positive, or negative reaction forms an interaction, and a collection of many such interactions forms a signed network. In addition, the evolution of information on social networks strongly relies on the nature of interactions between the users. The study of interactions is, therefore, crucial to predict the extent and nature of information spread. In this work, we study the relationship between users whether they agree or disagree in the dynamic evolution of interactions (cascades) on a larger network, Gab, to predict the relationship between the users on the social network. We quantitatively use the combination of text information and network information to enhance state of the art deep learning models for contradiction detection. The outcome of this research might contribute to improving link prediction.
590 $a School code: 0694.
650 4 $a Computer science. $3 523869
650 4 $a Social networks. $3 556983
653 $a Combined
653 $a Dynamic
653 $a Embeddings
653 $a Networks
690 $a 0984
690 $a 0626
710 2 $a The University of North Carolina at Charlotte. $b Computer Science. $3 3181506
773 0 $t Masters Abstracts International $g 82-02.
790 $a 0694
791 $a M.S.
792 $a 2020
793 $a English
856 4 0 $u https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28086851