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LEADER 03254cam a2200517Ii 4500

001 on1105587094;

003 OCoLC;

005 20191018095001.0;

006 m o d ;

007 cr unu||||||||;

008 190624s2019 ncua obm 000 0 eng d;

035 a| (Sirsi) o1105587094;

035 a| (OCoLC)1105587094;

049 a| EREE;

090 a| HM741;

100 1 a| Taghikhani, Shahin,e| author.?| UNAUTHORIZED;

245 10 a| Personality assessment from social media data :b| an ensemble model /c| by Shahinn Taghikhani.;

264 1 a| [Greenville, N.C.] :b| [East Carolina University],c| 2019.;

300 a| 74 pages :b| illustrations (chiefly color);

336 a| textb| txt2| rdacontent;

337 a| computerb| c2| rdamedia;

338 a| online resourceb| cr2| rdacarrier;

347 a| text fileb| PDFc| 2.074 MB2| rda;

538 a| System requirements: Adobe Reader.;

538 a| Mode of access: World Wide Web.;

502 b| M.S.c| East Carolina Universityd| 2019.;

500 a| Presented to the faculty of the Department of Computer Science;

500 a| Advisor: Nasseh Tabrizi;

500 a| Title from PDF t.p. (viewed October 15, 2019).;

520 3 a| The global prevalence of social media encourages people to upload and share a vast and recurrent amount of information about themselves through various mediums of communication such as text, pictures, audio, and video. These means of communication are embedded with people's interests, emotions, values and personality that can be collected from this data. This significant amount of information has become the interest of different professionals and industries such as data scientists, psychologist, businesses, etc., primarily to predict the behavior, traits and potential interests of people on online platforms to offer various products or services to users geared towards their benefit. Our research findings reported in this thesis indicate that the personality of a user can be assessed through analyzing their photos on social media, and is supported by a review of relevant literature published since 1999 and a comparative study and performance analysis using evaluation metrics over twelve state-of-the-art research studies published from 2016. As a result, this thesis introduces a new ensemble model that achieves the improved accuracy for each personality attire and shows that using an optimized feature space improves prediction performance.;

504 a| Includes bibliographical references.;

650 0 a| Social media.=| ^A770054;

650 0 a| Personality assessment.=| ^A3792;

650 0 a| Machine learning.=| ^A145211;

653 a| Deep Learning;

653 a| Ensemble Model;

700 1 a| Tabrizi, M. H. N.,e| degree supervisor.?| UNAUTHORIZED;

710 2 a| East Carolina University.b| Department of Computer Science.?| UNAUTHORIZED;

856 40 z| Access via ScholarShipu| http://hdl.handle.net/10342/7278;

949 o| wjh;

994 a| C0b| ERE;

596 a| 1 4;

998 a| 5111071;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5111071-1001l| JNETm| JOYNERr| Ys| Yt| JNE3ETDu| 6/24/2019x| ETDz| JERESOURCE;

999 a| CLICK ON WEB ADDRESSw| ASISc| 1i| 5111071-2001l| HSLELECm| HSLr| Ys| Yt| HEETDu| 6/24/2019x| ETDz| HERESOURCE;

Personality assessment from social media data : an ensemble model / by Shahinn Taghikhani.

Author/creator	Taghikhani, Shahin author.
Other author	Tabrizi, M. H. N., degree supervisor.
Other author	East Carolina University. Department of Computer Science.
Format	Theses and dissertations
Publication	[Greenville, N.C.] : [East Carolina University], 2019.
Description	74 pages : illustrations (chiefly color)
Supplemental Content	Access via ScholarShip
Subjects	Social media. Personality assessment. Machine learning.

Summary	The global prevalence of social media encourages people to upload and share a vast and recurrent amount of information about themselves through various mediums of communication such as text, pictures, audio, and video. These means of communication are embedded with people's interests, emotions, values and personality that can be collected from this data. This significant amount of information has become the interest of different professionals and industries such as data scientists, psychologist, businesses, etc., primarily to predict the behavior, traits and potential interests of people on online platforms to offer various products or services to users geared towards their benefit. Our research findings reported in this thesis indicate that the personality of a user can be assessed through analyzing their photos on social media, and is supported by a review of relevant literature published since 1999 and a comparative study and performance analysis using evaluation metrics over twelve state-of-the-art research studies published from 2016. As a result, this thesis introduces a new ensemble model that achieves the improved accuracy for each personality attire and shows that using an optimized feature space improves prediction performance.
General note	Presented to the faculty of the Department of Computer Science
General note	Advisor: Nasseh Tabrizi
General note	Title from PDF t.p. (viewed October 15, 2019).
Dissertation note	M.S. East Carolina University 2019.
Bibliography note	Includes bibliographical references.
Technical details	System requirements: Adobe Reader.
Technical details	Mode of access: World Wide Web.

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Personality assessment from social media data : an ensemble model / by Shahinn Taghikhani.

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