Towards a Methodology for Social Business Intelligence in the Era of Big Social Data Incorporating Trust and Semantic Analysis

Abstract

Business intelligence applications support decision makers by providing meaningful information from extracted data mainly coming from operational databases and structured data sources. However, the volume of unstructured data is growing very fast especially when analysing external data such as customers’ reviews in social media. It is essential to determine the reputation of the source to the analysts, so that they can take into account the trust value of each source in their analysis. Another important consideration is the semantics of extracted textual data from which meaningful information is derived. The aim of this paper is to provide readers with an understanding of the central concepts and the current state-of-the-art in social trust and semantic analysis of big social data. We provide an in depth analysis of existing challenges and identify set of quality attributes to be used as guide for designing and evaluating architectures of big social trust.