The Resource Identifying personality and topics of social media, Trinadha Rajeswari Muppala

Identifying personality and topics of social media, Trinadha Rajeswari Muppala

Label
Identifying personality and topics of social media
Title
Identifying personality and topics of social media
Statement of responsibility
Trinadha Rajeswari Muppala
Creator
Contributor
Author
Degree supervisor
Subject
Genre
Language
eng
Summary
Twitter and Facebook are the renowned social networking platforms where users post, share, interact and express to the world, their interests, personality, and behavioral information. User-created content on social media can be a source of truth, which is suitable to be consumed for the personality identification of social media users. Personality assessment using the Big 5 personality factor model benefits organizations in identifying potential professionals, future leaders, best-fit candidates for the role, and build effective teams. Also, the Big 5 personality factors help to understand depression symptoms among aged people in primary care. We had hypothesized that understanding the user personality of the social network would have significant benefits for topic modeling of different areas like news, towards understanding community interests, and topics. In this thesis, we will present a multi-label personality classification of the social media data and topic feature classification model based on the Big 5 model. We have built the Big 5 personality classification model using a Twitter dataset that has defined openness, conscientiousness, extraversion, agreeableness, and neuroticism. In this thesis, we (1) conduct personality detection using the Big 5 model, (2) extract the topics from Facebook and Twitter data based on each personality, (3) analyze the top essential topics, and (4) find the relation between topics and personalities. The personality would be useful to identify what kind of personality, which topics usually talk about in social media. Multi-label classification is done using Multinomial Naïve Bayes, Logistic Regression, Linear SVC. Topic Modeling is done based on LDA and KATE. Experimental results with Twitter and Facebook data demonstrate that the proposed model has achieved promising results
Cataloging source
UMK
http://library.link/vocab/creatorName
Muppala, Trinadha Rajeswari
Degree
M.S.
Dissertation year
2019.
Granting institution
School of Computing and Engineering. University of Missouri-Kansas City,
Illustrations
illustrations
Index
no index present
Literary form
non fiction
Nature of contents
  • dictionaries
  • bibliography
  • theses
http://library.link/vocab/relatedWorkOrContributorDate
1960-
http://library.link/vocab/relatedWorkOrContributorName
Lee, Yugyung
http://library.link/vocab/subjectName
  • Social media
  • Personality assessment
  • Machine learning
Label
Identifying personality and topics of social media, Trinadha Rajeswari Muppala
Instantiates
Publication
Copyright
Note
  • "A thesis in Computer Science."
  • Advisor: Yugyung Lee
  • Vita
Antecedent source
not applicable
Bibliography note
Includes bibliographical references (pages 37-39)
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
black and white
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
Introduction -- Background and related work -- Proposed framework -- Results and evaluations -- Conclusion and future work
Control code
1137540119
Dimensions
unknown
Extent
1 online resource (40 pages)
File format
one file format
Form of item
online
Level of compression
mixed
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
illustrations.
Quality assurance targets
not applicable
Specific material designation
remote
System control number
(OCoLC)1137540119
System details
  • The full text of the thesis is available as an Adobe Acrobat .pdf file; Adobe Acrobat Reader required to view the file
  • Mode of access: World Wide Web
Label
Identifying personality and topics of social media, Trinadha Rajeswari Muppala
Publication
Copyright
Note
  • "A thesis in Computer Science."
  • Advisor: Yugyung Lee
  • Vita
Antecedent source
not applicable
Bibliography note
Includes bibliographical references (pages 37-39)
Carrier category
online resource
Carrier category code
  • cr
Carrier MARC source
rdacarrier
Color
black and white
Content category
text
Content type code
  • txt
Content type MARC source
rdacontent
Contents
Introduction -- Background and related work -- Proposed framework -- Results and evaluations -- Conclusion and future work
Control code
1137540119
Dimensions
unknown
Extent
1 online resource (40 pages)
File format
one file format
Form of item
online
Level of compression
mixed
Media category
computer
Media MARC source
rdamedia
Media type code
  • c
Other physical details
illustrations.
Quality assurance targets
not applicable
Specific material designation
remote
System control number
(OCoLC)1137540119
System details
  • The full text of the thesis is available as an Adobe Acrobat .pdf file; Adobe Acrobat Reader required to view the file
  • Mode of access: World Wide Web

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