Social Credibility Incorporating Semantic Analysis and Machine Learning: A Survey of the State-of-the-Art and Future Research Directions

Bilal Abu-Salih; Bushra Bremie; Pornpit Wongthongtham; Kevin Duan; Tomayess Issa; Kit Yan Chan; Mohammad Alhabashneh; Teshreen Albtoush; Sulaiman Alqahtani; Abdullah Alqahtani; Muteeb Alahmari; Naser Alshareef; Abdulaziz Albahlal

arXiv · 1902.10402

Social Credibility Incorporating Semantic Analysis and Machine Learning: A Survey of the State-of-the-Art and Future Research Directions

Bilal Abu-Salih·Bushra Bremie·Pornpit Wongthongtham·Kevin Duan·Tomayess Issa·Kit Yan Chan·Mohammad Alhabashneh·Teshreen Albtoush·Sulaiman Alqahtani·Abdullah Alqahtani·Muteeb Alahmari·Naser Alshareef·Abdulaziz Albahlal

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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 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.

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Bilal Abu-Salih, Bushra Bremie, Pornpit Wongthongtham, Kevin Duan, Tomayess Issa, Kit Yan Chan, Mohammad Alhabashneh, Teshreen Albtoush, Sulaiman Alqahtani, Abdullah Alqahtani, Muteeb Alahmari, Naser Alshareef, Abdulaziz Albahlal. 2019-02-27. Social Credibility Incorporating Semantic Analysis and Machine Learning: A Survey of the State-of-the-Art and Future Research Directions. https://arxiv.org/abs/1902.10402

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