Building a contextual dimension for OLAP using textual data from social networks

Due to the continuous growth of social networks the textual information available has increased exponentially. Data warehouses (DW) and online analytical processing (OLAP) are some of the established technologies to process and analyze structured data. However, one of their main limitations is the lack of automatic processing and analysis of unstructured data (specifically, textual data), and its integration with structured data. This paper proposes the creation, integration and implementation of a new dimension called Contextual Dimension from texts obtained from social networks into a multidimensional model. Such a dimension is automatically created after applying hierarchical clustering algorithms and is fully independent from the language of the texts. This dimension allows the inclusion of multidimensional analysis of texts using contexts and topics integrated with conventional dimensions into business decisions. The experiments were carried out by means of a freeware OLAP system (Wonder 3.0) using real data from social networks.