Analyze Unstructured Data Patterns for Conceptual Representation
This provides a solution for users seeking a homogeneous and updated news source with improved navigation, though it appears incremental in applying existing conceptual analysis methods to news data.
The study tackled the problem of aggregating and representing online news from multiple sources by developing a mobile app that analyzes unstructured data patterns to discover main concepts and structure them into a tree-based interface, resulting in a new conceptual framework for easier and faster news navigation.
Online news media provides aggregated news and stories from different sources all over the world and up-to-date news coverage. The main goal of this study is to have a solution that considered as a homogeneous source for the news and to represent the news in a new conceptual framework. Furthermore, the user can easily find different updated news in a fast way through the designed interface. The Mobile App implementation is based on modeling the multi-level conceptual analysis discipline. Discovering main concepts of any domain is captured from the hidden unstructured data that are analyzed by the proposed solution. Concepts are discovered through analyzing data patterns to be structured into a tree-based interface for easy navigation for the end user, through the discovered news concepts. Our final experiment results showing that analyzing the news before displaying to the end-user and restructuring the final output in a conceptual multilevel structure, that producing new display frame for the end user to find the related information to his interest.