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

TL;DR

This survey reviews current methods integrating semantic analysis and machine learning for assessing social credibility, highlighting research gaps and proposing future directions in social big data analysis.

Contribution

It provides a comprehensive overview of state-of-the-art techniques in social credibility assessment using semantic and machine learning approaches, and suggests future research pathways.

Findings

Current approaches vary in effectiveness and scope

Identifies key research gaps in social credibility analysis

Recommends future research directions in the field

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.