Show simple item record

dc.contributor.authorAhmad, Hussain
dc.contributor.authorAsgha, Muhammad Zubair
dc.contributor.authorAlotaibi, Fahad M.
dc.contributor.authorHameed, Ibrahim A.
dc.date.accessioned2020-09-04T13:24:53Z
dc.date.available2020-09-04T13:24:53Z
dc.date.created2020-08-17T20:52:15Z
dc.date.issued2020
dc.identifier.citationJournal of Medical Imaging and Health Informatics. 2020, 10 (10), 2446-2451.en_US
dc.identifier.issn2156-7018
dc.identifier.urihttps://hdl.handle.net/11250/2676487
dc.description.abstractIn social media, depression identification could be regarded as a complex task because of the complicated nature associated with mental disorders. In recent times, there has been an evolution in this research area with growing popularity of social media platforms as these have become a fundamental part of people's day-to-day life. Social media platforms and their users share a close relationship due to which the users' personal life is reflected in these platforms on several levels. Apart from the associated complexity in recognising mental illnesses via social media platforms, implementing supervised machine learning approaches like deep neural networks is yet to be adopted in a large scale because of the inherent difficulties associated with procuring sufficient quantities of annotated training data. Because of such reasons, we have made effort to identify deep learning model that is most effective from amongst selected architectures with previous successful record in supervised learning methods. The selected model is employed to recognise online users that display depression; since there is limited unstructured text data that could be extracted from Twitter.en_US
dc.language.isoengen_US
dc.publisherAmerican Scientific Publishersen_US
dc.titleApplying Deep Learning Technique for Depression Classification in Social Media Texten_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionacceptedVersionen_US
dc.source.pagenumber2446-2451en_US
dc.source.volume10en_US
dc.source.journalJournal of Medical Imaging and Health Informaticsen_US
dc.source.issue10en_US
dc.identifier.doihttps://doi.org/10.1166/jmihi.2020.3169
dc.identifier.cristin1823715
dc.description.localcodeThis article will not be available due to copyright restrictions by American Scientific Publishers . https://doi.org/10.1166/jmihi.2020.3169en_US
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1


Files in this item

Thumbnail

This item appears in the following Collection(s)

Show simple item record