Towards a semantic data infrastructure for social Business Intelligence
Rafael Berlanga LLavori (Universitat Jaume I, Spain)
Abstract: The enormous 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 confined to corporate data with little or null interaction 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 are 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 talk aims to introduce these issues and to devise potential solutions for the near future. More specifically, the talk will focus on the proposal of a semantic data infrastructure for BI aimed at providing new opportunities for integrating traditional and social BI.
- Subjective Business Polarization: Sentiment Analysis meets Predictive Modeling (Furio Camillo and Caterina Liberati)
- Sentiment Analysis and City Branding (Roberto Grandi and Federico Neri)
- Integrating Spatial Decision Supports and Social Networks (Nicoletta Dessì and Gianfranco Garau)
- OLAP on Information Networks: a new Framework for Dealing with Bibliographic Data (Wararat Jakawat, Cécile Favre, and Sabine Loudcher)
- Towards a semantic data infrastructure for Social Business Intelligence (Rafael Berlanga)