Towards a Semantic Data Infrastructure for Social Business Intelligence

Abstract

The tremendous popularity of web-based social media is attracting the attention of the industry to take profit from the massive availability of sentiment data, which is considered of high value for Business Intelligence (BI). So far, BI has been mainly concerned with corporate data with little or null attention with the external world. However, for BI analysts, taking into account the Voice of the Customer (VoC) and the Voice of the Market (VoM) is crucial for putting in context the results of their analyses. Recent advances in Opinion Mining and Sentiment Analysis have made possible to effectively extract and summarize sentiment data from these massive social media. As a consequence, VoC and VoM can be now listened from web-based social media (e.g., blogs, reviews forums, social networks, and so on). However, new challenges arise when attempting to integrate traditional corporate data and external sentiment data. This paper aims to introduce these issues and to devise potential solutions for the near future. More specifically, the paper will focus on the proposal of a semantic data infrastructure for BI aimed at providing new opportunities for integrating traditional and social BI.KeywordsSocial Business IntelligenceLinked DataSentiment Analysis