Storing and analysing voice of the market data in the corporate data warehouse
Web opinion feeds have become one of the most popular information sources users consult before buying products or contracting services. Negative opinions about a product can have a high impact in its sales figures. As a consequence, companies are more and more concerned about how to integrate opinion data in their business intelligence models so that they can predict sales figures or define new strategic goals. After analysing the requirements of this new application, this paper proposes a multidimensional data model to integrate sentiment data extracted from opinion posts in a traditional corporate data warehouse. Then, a new sentiment data extraction method that applies semantic annotation as a means to facilitate the integration of both types of data is presented. In this method, Wikipedia is used as the main knowledge resource, together with some well-known lexicons of opinion words and other corporate data and metadata stores describing the company products like, for example, technical specifications and user manuals. The resulting information system allows users to perform new analysis tasks by using the traditional OLAP-based data warehouse operators. We have developed a case study over a set of real opinions about digital devices which are offered by a wholesale dealer. Over this case study, the quality of the extracted sentiment data is evaluated, and some query examples that illustrate the potential uses of the integrated model are provided.
data warehouses sentiment analysis olap text processing