Towards suicide prevention: early detection of depression on social media
Mostra el registre complet Registre parcial de l'ítem
- dc.contributor.author Leiva Aranda, Víctorca
- dc.date.accessioned 2017-10-27T10:57:45Z
- dc.date.available 2017-10-27T10:57:45Z
- dc.date.issued 2017-10-27
- dc.description Supervisor: Ana Maria Freire Veiga
- dc.description Treball fi de màster de: Master in Intelligent Interactive Systems
- dc.description.abstract The statistics presented by the World Health Organization inform that 90% of the suicides can be attributed to mental illnesses in high-income countries. Besides, previous studies concluded that people with mental illnesses tend to reveal their mental condition on social media, as a way of relief. Among all these users of social media platforms, adolescents are the most frequent ones. Hence, these previous studies drive us through the detection of depression on social media as a first step against online suicidal behaviour. Thus, the main objective of this work is the analysis of the messages that user posts online, sequentially through a time period, and detect as soon as possible if the user is at risk of depression. Our preliminary experiments report the impact of sentiment analysis techniques and a combination of machine learning algorithms for detecting users with depression in Reddit.ca
- dc.format.mimetype application/pdfca
- dc.identifier.uri http://hdl.handle.net/10230/33112
- dc.language.iso engca
- dc.rights Atribución-NoComercial-SinDerivadas 3.0 Españaca
- dc.rights.accessRights info:eu-repo/semantics/openAccessca
- dc.rights.uri http://creativecommons.org/licenses/by-nc-nd/3.0/es/ca
- dc.subject.other Suïcidi
- dc.subject.other Mitjans de comunicació social
- dc.subject.other Malalties mentals
- dc.subject.other Depressió psíquica -- Diagnòstic
- dc.title Towards suicide prevention: early detection of depression on social mediaca
- dc.type info:eu-repo/semantics/masterThesisca