國立高雄大學圖資館 |

登入

回首頁

Machine Learning over User-Generated Content: From Unsupervised User Behavioral Models to Emotion Recognition via Deep Learning.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Machine Learning over User-Generated Content: From Unsupervised User Behavioral Models to Emotion Recognition via Deep Learning.
作者:	Rajabi, Zahra.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, 2021
面頁冊數:	124 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
附註:	Advisor: Shehu, Amarda.
Contained By:	Dissertations Abstracts International83-03B.
標題:	Computer science.
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28493581
ISBN:	9798538125623

Machine Learning over User-Generated Content: From Unsupervised User Behavioral Models to Emotion Recognition via Deep Learning.
Rajabi, Zahra.

Machine Learning over User-Generated Content: From Unsupervised User Behavioral Models to Emotion Recognition via Deep Learning. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 124 p.

Source: Dissertations Abstracts International, Volume: 83-03, Section: B.

Thesis (Ph.D.)--George Mason University, 2021.

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

We are now a machine-mediated society, with virtually every transaction and decision mediated, analyzed, and even recommended by algorithms running in the background. An increasing percentage of our interactions and societal discourse now happen over social media platforms. While this has increased our connectivity, transcending geopolitical borders, it has also provided us with unique problems that we would not have anticipated even a decade ago. The propagation of misinformation on social networks is now a societal problem and is prompting an increasing body of research into how to identify misinformation, how to identify spreaders of such information and, perhaps more importantly, how to design mitigation and intervention strategies. In this dissertation we show that advancing research to address these challenges requires foundational research into automated user behavioral profiles. We present an unsupervised learning algorithm that leverages user-generated con-tent to understand and categorize user behavior in near-real time. This line of work rests fundamentally on the ability of machines to understand language. This, in itself, is a multi-faceted challenge that has spawned entire domains in computer science, such as natural language processing. To advance the ability of machines to understand content, we focus on another fundamental problem, emotion recognition from short text in platforms, such as Twitter and Reddit. We significantly advance this line of research by going beyond binary sentiments and present sophisticated deep neural network-based models that can capture fine-grained emotions in the presence of a host of challenges, including data imbalance and noise due to human annotations. Finally, we ask a fundamental question of whether text is sufficient to understand emotions and show that supplementing text with new modes of interaction, such as emojis and emoticons, advances the ability of machines to disambiguate emotions, much like humans. We believe that the research presented in this dissertation lays the groundwork for further advancing machine understanding of human behavior in social media platforms.

ISBN: 9798538125623Subjects--Topical Terms:

199325
Computer science.
Subjects--Index Terms:

Deep Learning

Machine Learning over User-Generated Content: From Unsupervised User Behavioral Models to Emotion Recognition via Deep Learning.
LDR:03562nmm a2200445 4500 001 616424
005 20220513114333.5
008 220920s2021 ||||||||||||||||| ||eng d
020 $a 9798538125623
035 $a (MiAaPQ)AAI28493581
035 $a AAI28493581
040 $a MiAaPQ $c MiAaPQ
100 1 $a Rajabi, Zahra. $0 (orcid)0000-0003-2909-0188 $3 915665
245 1 0 $a Machine Learning over User-Generated Content: From Unsupervised User Behavioral Models to Emotion Recognition via Deep Learning.
260 1 $a Ann Arbor : $b ProQuest Dissertations & Theses, $c 2021
300 $a 124 p.
500 $a Source: Dissertations Abstracts International, Volume: 83-03, Section: B.
500 $a Advisor: Shehu, Amarda.
502 $a Thesis (Ph.D.)--George Mason University, 2021.
506 $a This item must not be sold to any third party vendors.
520 $a We are now a machine-mediated society, with virtually every transaction and decision mediated, analyzed, and even recommended by algorithms running in the background. An increasing percentage of our interactions and societal discourse now happen over social media platforms. While this has increased our connectivity, transcending geopolitical borders, it has also provided us with unique problems that we would not have anticipated even a decade ago. The propagation of misinformation on social networks is now a societal problem and is prompting an increasing body of research into how to identify misinformation, how to identify spreaders of such information and, perhaps more importantly, how to design mitigation and intervention strategies. In this dissertation we show that advancing research to address these challenges requires foundational research into automated user behavioral profiles. We present an unsupervised learning algorithm that leverages user-generated con-tent to understand and categorize user behavior in near-real time. This line of work rests fundamentally on the ability of machines to understand language. This, in itself, is a multi-faceted challenge that has spawned entire domains in computer science, such as natural language processing. To advance the ability of machines to understand content, we focus on another fundamental problem, emotion recognition from short text in platforms, such as Twitter and Reddit. We significantly advance this line of research by going beyond binary sentiments and present sophisticated deep neural network-based models that can capture fine-grained emotions in the presence of a host of challenges, including data imbalance and noise due to human annotations. Finally, we ask a fundamental question of whether text is sufficient to understand emotions and show that supplementing text with new modes of interaction, such as emojis and emoticons, advances the ability of machines to disambiguate emotions, much like humans. We believe that the research presented in this dissertation lays the groundwork for further advancing machine understanding of human behavior in social media platforms.
590 $a School code: 0883.
650 4 $a Computer science. $3 199325
650 4 $a Artificial intelligence. $3 194058
650 4 $a Language. $3 180769
650 4 $a Information technology. $3 184390
650 4 $a Information science. $3 190425
653 $a Deep Learning
653 $a Emotion recognition
653 $a Human behavior modeling
653 $a Machine Learning
653 $a Natural Language Processing
653 $a User-generated content
653 $a Emotion
653 $a Media platforms
653 $a Social networks
690 $a 0984
690 $a 0800
690 $a 0679
690 $a 0489
690 $a 0723
710 2 $a George Mason University. $b Information Technology. $3 915666
773 0 $t Dissertations Abstracts International $g 83-03B.
790 $a 0883
791 $a Ph.D.
792 $a 2021
793 $a English
856 4 0 $u http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28493581