Social Credibility Incorporating Semantic Analysis and Machine Learning: A Survey of the State-of-the-Art and Future Research Directions
Access Status
Open access
Authors
Abu Salih, Bilal
Bremie, B.
Clark, Ponnie
Duan, K.
Issa, Tomayess
Chan, Kit Yan
Alhabashneh, M.
Albtoush, T.
Alqahtani, S.
Alqahtani, A.
Alahmari, M.
Alshareef, N.
Albahlal, A.
Date
2019Type
Conference Paper
Metadata
Show full item recordCitation
Abu-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.
Source Title
Advances in Intelligent Systems and Computing
ISBN
ISSN
Faculty
Faculty of Humanities
Faculty of Business and Law
Faculty of Science and Engineering
School
School of Management
School of Design and the Built Environment
School of Electrical Engineering, Computing and Mathematical Sciences (EECMS)
Collection
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.