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dc.contributor.advisorDe Castro, Joel T.
dc.contributor.authorDeocampo, Nikie Jo E.
dc.date.accessioned2022-09-14T09:32:31Z
dc.date.available2022-09-14T09:32:31Z
dc.date.issued2019-04
dc.identifier.citationDeocampo, N. J. E. (2019). Classifier model based on text mining for discovery of depression on social media [Master’s thesis, West Visayas State University]. WVSU Institutional Repository and Electronic Dissertations and Theses PLUS.en
dc.identifier.urihttp://repository.wvsu.edu.ph/handle/123456789/146
dc.description.abstractPredicting suicidal people in social networks is a real social issue. Suicide due to depression or anxiety has always been a problem with strong socio-economic consequences. However, global provisions and services for identifying, supporting, and treating mental illness of this nature have been considered as insufficient. In this study, the researcher describes a complete process to dynamically collect suspected tweets according to a lexicon of topics persons with general depression and anxiety would usually post and talk about. The researcher provides a mechanism that automatically captures tweets indicating depression risk behaviors based on Sentiment Analysis and Natural Language Processing and each tweet is based on the polarity or sentiment weight of positive, negative or neutral. In building the model, Five Classification methods will be used for training and testing the data set. The Decision Tree algorithm provided the highest accuracy and precision which is followed closely by Naive Bayesian. The model with the highest accuracy will then be used to assist or support mental health test and decision support systems. The model was then tested and evaluated by a practicing psychologist using a same data set used for testing and training.en
dc.format.extentxi, 77 p. : ill. (col.).en
dc.language.isoenen
dc.publisherWest Visayas State Universityen
dc.subjectNaive Bayesianen
dc.subjectClassifier modelen
dc.subjectMental illnessen
dc.subjectTweetsen
dc.subjectLexiconen
dc.subject.lcshText data miningen
dc.subject.lcshDepressionsen
dc.subject.lcshSocial mediaen
dc.subject.lcshSentiment analysisen
dc.subject.lcshNatural language processingen
dc.subject.lcshDecision treesen
dc.subject.lcshSuicideen
dc.subject.lcshAnxietyen
dc.subject.lcshSuicide--Preventionen
dc.subject.lcshSocial networks--Psychological aspectsen
dc.subject.lcshSocial networksen
dc.subject.lcshOnline social networksen
dc.titleClassifier model based on text mining for discovery of depression on social mediaen
dc.typeThesisen
dcterms.accessRightsLimited public accessen
thesis.degree.disciplineCollege of Information and Communications Technologyen
thesis.degree.grantorWest Visayas State Universityen
thesis.degree.levelMastersen
thesis.degree.nameMaster in Information Technologyen
dc.contributor.corporateauthorWest Visayas State Universityen
dc.contributor.chairDe Castro, Joel T.
dc.contributor.committeememberGerardo, Bobby D.
dc.contributor.committeememberConcepcion, Ma. Beth S.
dc.contributor.committeememberSansolis, Evans D.
dc.contributor.committeememberDuran, Peter Rey
dc.subject.sdgSDG 3 - Good health and well-being


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