Classifier model based on text mining for discovery of depression on social media
dc.contributor.advisor | De Castro, Joel T. | |
dc.contributor.author | Deocampo, Nikie Jo E. | |
dc.date.accessioned | 2022-09-14T09:32:31Z | |
dc.date.available | 2022-09-14T09:32:31Z | |
dc.date.issued | 2019-04 | |
dc.identifier.citation | Deocampo, 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.uri | http://repository.wvsu.edu.ph/handle/123456789/146 | |
dc.description.abstract | Predicting 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.extent | xi, 77 p. : ill. (col.). | en |
dc.language.iso | en | en |
dc.publisher | West Visayas State University | en |
dc.subject | Naive Bayesian | en |
dc.subject | Classifier model | en |
dc.subject | Mental illness | en |
dc.subject | Tweets | en |
dc.subject | Lexicon | en |
dc.subject.lcsh | Text data mining | en |
dc.subject.lcsh | Depressions | en |
dc.subject.lcsh | Social media | en |
dc.subject.lcsh | Sentiment analysis | en |
dc.subject.lcsh | Natural language processing | en |
dc.subject.lcsh | Decision trees | en |
dc.subject.lcsh | Suicide | en |
dc.subject.lcsh | Anxiety | en |
dc.subject.lcsh | Suicide--Prevention | en |
dc.subject.lcsh | Social networks--Psychological aspects | en |
dc.subject.lcsh | Social networks | en |
dc.subject.lcsh | Online social networks | en |
dc.title | Classifier model based on text mining for discovery of depression on social media | en |
dc.type | Thesis | en |
dcterms.accessRights | Limited public access | en |
thesis.degree.discipline | College of Information and Communications Technology | en |
thesis.degree.grantor | West Visayas State University | en |
thesis.degree.level | Masters | en |
thesis.degree.name | Master in Information Technology | en |
dc.contributor.corporateauthor | West Visayas State University | en |
dc.contributor.chair | De Castro, Joel T. | |
dc.contributor.committeemember | Gerardo, Bobby D. | |
dc.contributor.committeemember | Concepcion, Ma. Beth S. | |
dc.contributor.committeemember | Sansolis, Evans D. | |
dc.contributor.committeemember | Duran, Peter Rey | |
dc.subject.sdg | SDG 3 - Good health and well-being |
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2. Master's Theses [50]
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Master's Thesis [2]