國立虎尾科技大學 |

Modeling Events and Affects in Social Media Stories.

Record Type:	Language materials, manuscript : Monograph/item
Title/Author:	Modeling Events and Affects in Social Media Stories./
Author:	Rahimtoroghi, Elahe.
Description:	1 online resource (154 pages)
Notes:	Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.
Contained By:	Dissertation Abstracts International79-09B(E).
Subject:	Computer science. -
Online resource:	click for full text (PQDT)
ISBN:	9780355865110

Modeling Events and Affects in Social Media Stories.
Rahimtoroghi, Elahe.

Modeling Events and Affects in Social Media Stories. - 1 online resource (154 pages)

Source: Dissertation Abstracts International, Volume: 79-09(E), Section: B.

Thesis (Ph.D.)--University of California, Santa Cruz, 2018.

Includes bibliographical references

Stories play an important role in human perception of the world and therefore the computational analysis of narrative structure is a key area in natural language processing. The focus of this thesis is to develop and evaluate computational models for two main elements of the narrative structure: Events and Desires. Our work first aims to test a theory that proposes a linear structure of narratives and identifies different parts of a story based on their function. Unlike most of the previous work that use the news articles or other simpler and more conventional genres, we use a corpus of personal stories from social media that have a wider range of topical content and variations of discourse relations. We present an unsupervised method for modeling narrative events, focusing on specific event relations based on the Penn Discourse Treebank's definition of contingency. We use a weakly supervised approach to extract the key events from stories and create a topic-sorted corpus of personal narratives using a bootstrapping method. We additionally propose new evaluation methods for testing the contingent event pairs. Our results show that most of the relations we learn from blog stories are not found in the existing event collections. In our final contribution, we develop supervised methods for modeling the protagonist's goals and their outcome in personal narratives, as a sub-problem of modeling affects. Our studies show that both prior and post context are useful for modeling desire fulfillment. In addition, we show that exploiting narrative structure is helpful, both directly in terms of the utility of discourse relation features and indirectly by using a sequential model. We further examine our analysis of the human desires by identifying and studying the expressions of unfulfilled goals.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2018

Mode of access: World Wide Web

ISBN: 9780355865110Subjects--Topical Terms:

573171
Computer science.
Index Terms--Genre/Form:

554714
Electronic books.

Modeling Events and Affects in Social Media Stories.
LDR:03014ntm a2200325Ki 4500 001 919002
005 20181106103645.5
006 m o u
007 cr mn||||a|a||
008 190606s2018 xx obm 000 0 eng d
020 $a 9780355865110
035 $a (MiAaPQ)AAI10784306
035 $a (MiAaPQ)ucsc:11465
035 $a AAI10784306
040 $a MiAaPQ $b eng $c MiAaPQ $d NTU
100 1 $a Rahimtoroghi, Elahe. $3 1193473
245 1 0 $a Modeling Events and Affects in Social Media Stories.
264 0 $c 2018
300 $a 1 online resource (154 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: Dissertation Abstracts International, Volume: 79-09(E), Section: B.
500 $a Adviser: Marilyn A. Walker.
502 $a Thesis (Ph.D.)--University of California, Santa Cruz, 2018.
504 $a Includes bibliographical references
520 $a Stories play an important role in human perception of the world and therefore the computational analysis of narrative structure is a key area in natural language processing. The focus of this thesis is to develop and evaluate computational models for two main elements of the narrative structure: Events and Desires. Our work first aims to test a theory that proposes a linear structure of narratives and identifies different parts of a story based on their function. Unlike most of the previous work that use the news articles or other simpler and more conventional genres, we use a corpus of personal stories from social media that have a wider range of topical content and variations of discourse relations. We present an unsupervised method for modeling narrative events, focusing on specific event relations based on the Penn Discourse Treebank's definition of contingency. We use a weakly supervised approach to extract the key events from stories and create a topic-sorted corpus of personal narratives using a bootstrapping method. We additionally propose new evaluation methods for testing the contingent event pairs. Our results show that most of the relations we learn from blog stories are not found in the existing event collections. In our final contribution, we develop supervised methods for modeling the protagonist's goals and their outcome in personal narratives, as a sub-problem of modeling affects. Our studies show that both prior and post context are useful for modeling desire fulfillment. In addition, we show that exploiting narrative structure is helpful, both directly in terms of the utility of discourse relation features and indirectly by using a sequential model. We further examine our analysis of the human desires by identifying and studying the expressions of unfulfilled goals.
533 $a Electronic reproduction. $b Ann Arbor, Mich. : $c ProQuest, $d 2018
538 $a Mode of access: World Wide Web
650 4 $a Computer science. $3 573171
655 7 $a Electronic books. $2 local $3 554714
690 $a 0984
710 2 $a ProQuest Information and Learning Co. $3 1178819
710 2 $a University of California, Santa Cruz. $b Computer Science. $3 1184383
773 0 $t Dissertation Abstracts International $g 79-09B(E).
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=10784306 $z click for full text (PQDT)