A Knowledge-Based Recommendation System That Includes Sentiment Analysis and Deep Learning

dc.contributor.authorRosa, Renata Lopes
dc.contributor.authorSchwartz, Gisele Maria [UNESP]
dc.contributor.authorRuggiero, Wilson Vicente
dc.contributor.authorRodrigue, Derndstenes Zegarra
dc.contributor.institutionUniversidade Federal de Lavras (UFLA)
dc.contributor.institutionUniversidade Estadual Paulista (Unesp)
dc.contributor.institutionUniversidade de São Paulo (USP)
dc.date.accessioned2019-10-04T15:23:58Z
dc.date.available2019-10-04T15:23:58Z
dc.date.issued2019-04-01
dc.description.abstractOnline social networks provide relevant information on users' opinion about different themes. Thus, applications, such as monitoring and recommendation systems (RS) can collect and analyze this data. This paper presents a knowledge-based recommendation system (KBRS), which includes an emotional health monitoring system to detect users with potential psychological disturbances, specifically, depression and stress. Depending on the monitoring results, the KBRS, based on ontologies and sentiment analysis, is activated to send happy, calm, relaxing, or motivational messages to users with psychological disturbances. Also, the solution includes a mechanism to send warning messages to authorized persons, in case a depression disturbance is detected by the monitoring system. The detection of sentences with depressive and stressful content is performed through a convolutional neural network and a bidirectional long short-term memory recurrent neural networks (RNN); the proposed method reached an accuracy of 0.89 and 0.90 to detect depressed and stressed users, respectively. Experimental results show that the proposed KBRS reached a rating of 94% of very satisfied users, as opposed to 69% reached by a RS without the use of neither a sentiment metric nor ontologies. Additionally, subjective test results demonstrated that the proposed solution consumes low memory, processing, and energy from current mobile electronic devices.en
dc.description.affiliationUniv Fed Lavras, BR-37200000 Lavras, MG, Brazil
dc.description.affiliationUniv Estadual Paulista, Biosci Inst Rio Claro, BR-13506900 Sao Paulo, Brazil
dc.description.affiliationUniv Sao Paulo, Polytech Sch, BR-05508010 Sao Paulo, Brazil
dc.description.affiliationUnespUniv Estadual Paulista, Biosci Inst Rio Claro, BR-13506900 Sao Paulo, Brazil
dc.format.extent2124-2135
dc.identifierhttp://dx.doi.org/10.1109/TII.2018.2867174
dc.identifier.citationIeee Transactions On Industrial Informatics. Piscataway: Ieee-inst Electrical Electronics Engineers Inc, v. 15, n. 4, p. 2124-2135, 2019.
dc.identifier.doi10.1109/TII.2018.2867174
dc.identifier.issn1551-3203
dc.identifier.urihttp://hdl.handle.net/11449/186268
dc.identifier.wosWOS:000467095500027
dc.language.isoeng
dc.publisherIeee-inst Electrical Electronics Engineers Inc
dc.relation.ispartofIeee Transactions On Industrial Informatics
dc.rights.accessRightsAcesso aberto
dc.sourceWeb of Science
dc.subjectDeep learning
dc.subjectknowledge personalization and customization
dc.subjectrecommendation system
dc.subjectsentiment analysis
dc.subjectsocial networks
dc.titleA Knowledge-Based Recommendation System That Includes Sentiment Analysis and Deep Learningen
dc.typeArtigo
dcterms.licensehttp://www.ieee.org/publications_standards/publications/rights/rights_policies.html
dcterms.rightsHolderIeee-inst Electrical Electronics Engineers Inc

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