An Unstructured Information Management Architecture Approach to Text Analytics of Cancer Blogs

Abstract

In this research the authors explore the potential of the Unstructured Information Management Architecture (UIMA) platform in text analytics of cancer blogs. The application is developed using the UIMA open source platform. They use the text analytics methods of categorization, clustering, taxonomic classification, and others to identify and analyze the patterns in cancer blog postings. The authors establish a comprehensive UIMA methodology for developing text analytics applications for the analysis of cancer blogs. Additional insights are extracted through the development of categories or keywords contained in the blogs, the development of a taxonomy and the examination of relationships among the categories. The application has the potential for generalizability and implementation with health content in other blogs and social media. It has the potential to provide insight and decision support for cancer management and to facilitate the efficient and relevant search for information on cancer.