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Data-driven development of personality predictive lexica from social media
Descriptive
TitleInfo
Title
Data-driven development of personality predictive lexica from social media
Name
(type = personal)
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(type = family)
He
NamePart
(type = given)
Xiaoli
NamePart
(type = date)
1990-
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Xiaoli He
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(authority = RULIB)
author
Name
(type = personal)
NamePart
(type = family)
de Melo
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Gerard
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Gerard de Melo
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Advisory Committee
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chair
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(type = personal)
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(type = family)
Singh
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Manish
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Manish Singh
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Advisory Committee
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internal member
Name
(type = personal)
NamePart
(type = family)
Zhang
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(type = given)
Yongfeng
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Yongfeng Zhang
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Advisory Committee
Role
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internal member
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Rutgers University
Role
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degree grantor
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School of Graduate Studies
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school
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Text
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theses
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2020
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2020-05
Language
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English
Abstract
(type = abstract)
Automatic personality prediction is getting more popular because it is convenient and reliable. Lexicon-based analysis has been successful in the fields of sentiment analysis and emotion. Many studies have used linear models for personality prediction, which suggests that we can also use lexical-based analysis for personality prediction. In the current study, we developed weighted word lexicons (words and scores) on each dimension of MBTI personality. The lexicons are built based on eight MBTI datasets, different features (unigram, 1-2 grams, 1-2-3 grams) and weightings (TF, TF-IDF, TF-logIDF), and different supervised learning models. Then we ran correlation analysis between our MBTI lexicons and other existing lexicons, such as Big-5, emotion, sentiment, age, gender. The correlation analysis shows interesting and reasonable correlation between different personality dimensions and other psychological traits, and it also provides evidence for the robustness of our lexicons.
Subject
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Topic
Personality prediction
Subject
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Topic
Computer Science
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Title
Rutgers University Electronic Theses and Dissertations
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ETD
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ETD_10657
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application/pdf
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text/xml
Extent
1 online resource (viii, 21 pages) : illustrations
Note
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M.S.
Note
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Includes bibliographical references
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TitleInfo
Title
School of Graduate Studies Electronic Theses and Dissertations
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rucore10001600001
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NjNbRU
Identifier
(type = doi)
doi:10.7282/t3-xdk1-hq74
Genre
(authority = ExL-Esploro)
ETD graduate
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Rights
RightsDeclaration
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The author owns the copyright to this work.
RightsHolder
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Name
FamilyName
He
GivenName
Xiaoli
Role
Copyright Holder
RightsEvent
Type
Permission or license
DateTime
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2020-03-27 14:26:37
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Name
Xiaoli He
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Copyright holder
Affiliation
Rutgers University. School of Graduate Studies
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Author Agreement License
Detail
I hereby grant to the Rutgers University Libraries and to my school the non-exclusive right to archive, reproduce and distribute my thesis or dissertation, in whole or in part, and/or my abstract, in whole or in part, in and from an electronic format, subject to the release date subsequently stipulated in this submittal form and approved by my school. I represent and stipulate that the thesis or dissertation and its abstract are my original work, that they do not infringe or violate any rights of others, and that I make these grants as the sole owner of the rights to my thesis or dissertation and its abstract. I represent that I have obtained written permissions, when necessary, from the owner(s) of each third party copyrighted matter to be included in my thesis or dissertation and will supply copies of such upon request by my school. I acknowledge that RU ETD and my school will not distribute my thesis or dissertation or its abstract if, in their reasonable judgment, they believe all such rights have not been secured. I acknowledge that I retain ownership rights to the copyright of my work. I also retain the right to use all or part of this thesis or dissertation in future works, such as articles or books.
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Embargo
DateTime
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2020-05-31
DateTime
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2021-05-31
Detail
Access to this PDF has been restricted at the author's request. It will be publicly available after May 31st, 2021.
Copyright
Status
Copyright protected
Availability
Status
Open
Reason
Permission or license
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ETD
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windows xp
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2020-06-12T13:02:31
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