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dc.contributor.authorAbu Salih, Bilal
dc.contributor.authorBremie, B.
dc.contributor.authorClark, Ponnie
dc.contributor.authorDuan, K.
dc.contributor.authorIssa, Tomayess
dc.contributor.authorChan, Kit Yan
dc.contributor.authorAlhabashneh, M.
dc.contributor.authorAlbtoush, T.
dc.contributor.authorAlqahtani, S.
dc.contributor.authorAlqahtani, A.
dc.contributor.authorAlahmari, M.
dc.contributor.authorAlshareef, N.
dc.contributor.authorAlbahlal, A.
dc.date.accessioned2020-01-18T15:30:12Z
dc.date.available2020-01-18T15:30:12Z
dc.date.issued2019
dc.identifier.citationAbu-Salih, B. and Bremie, B. and Wongthongtham, P. and Duan, K. and Issa, T. and Chan, K.Y. and Alhabashneh, M. et al. 2019. Social Credibility Incorporating Semantic Analysis and Machine Learning: A Survey of the State-of-the-Art and Future Research Directions. In Barolli L., Takizawa M., Xhafa F., Enokido T. (eds), Proceedings of the Workshops of the 33rd International Conference on Advanced Information Networking and Applications WAINA 2019, 27-29 Mar 2019. Matsue, Japan. Web, Artificial Intelligence and Network Applications, Vol 927, pp 887-986 Springer.
dc.identifier.urihttp://hdl.handle.net/20.500.11937/77681
dc.identifier.doi10.1007/978-3-030-15035-8_87
dc.description.abstract

The wealth of Social Big Data (SBD) represents a unique opportunity for organisations to obtain the excessive use of such data abundance to increase their revenues. Hence, there is an imperative need to capture, load, store, process, analyse, transform, interpret, and visualise such manifold social datasets to develop meaningful insights that are specific to an application’s domain. This paper lays the theoretical background by introducing the state-of-the-art literature review of the research topic. This is associated with a critical evaluation of the current approaches, and fortified with certain recommendations indicated to bridge the research gap.

dc.subjectcs.SI
dc.subjectcs.SI
dc.subjectcs.LG
dc.titleSocial Credibility Incorporating Semantic Analysis and Machine Learning: A Survey of the State-of-the-Art and Future Research Directions
dc.typeConference Paper
dcterms.source.volume927
dcterms.source.startPage887
dcterms.source.endPage896
dcterms.source.issn2194-5357
dcterms.source.titleAdvances in Intelligent Systems and Computing
dcterms.source.isbn9783030150341
dc.date.updated2020-01-18T15:30:11Z
curtin.departmentSchool of Management
curtin.departmentSchool of Design and the Built Environment
curtin.departmentSchool of Electrical Engineering, Computing and Mathematical Sciences (EECMS)
curtin.accessStatusOpen access
curtin.facultyFaculty of Humanities
curtin.facultyFaculty of Business and Law
curtin.facultyFaculty of Science and Engineering
curtin.contributor.orcidChan, Kit Yan [0000-0003-4949-7647]
curtin.contributor.orcidIssa, Tomayess [0000-0003-3460-4200]
curtin.contributor.orcidAbu Salih, Bilal [0000-0001-9875-4369]
curtin.contributor.researcheridIssa, Tomayess [H-2819-2014]
dcterms.source.eissn2194-5365
curtin.contributor.scopusauthoridChan, Kit Yan [55647800400]
curtin.contributor.scopusauthoridClark, Ponnie [6505990442]
curtin.contributor.scopusauthoridIssa, Tomayess [36523037100]


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