Analyze Unstructured Data Patterns for Conceptual Representation

TL;DR

This paper presents a novel approach to analyze unstructured news data, extracting main concepts to create a multi-level conceptual framework that enhances user navigation and information retrieval in news applications.

Contribution

It introduces a new method for modeling unstructured news data into a hierarchical conceptual structure for improved user experience.

Findings

Effective concept discovery from unstructured data

Enhanced news navigation through hierarchical visualization

Improved user engagement with structured news presentation

Abstract

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.