A Multi-dimensional Analysis and Data Cube for Unstructured Text and Social Media
Suan Lee, Nam‐Soo Kim, Jinho Kim · 2014
Recently, unstructured data like texts, documents, or SNS messages has been increasingly being used in many applications, rather than structured data consisting of simple numbers or characters. Thus it becomes more important to analysis unstructured text data to extract valuable information for usres decision making. Like OLAP (On-Line Analytical Processing) analysis over structured data, Multi-dimensional analysis for these unstructured data is popularly being required. To facilitate these analysis requirements on the unstructured data, a text cube model on multi-dimensional text database has been proposed. In this paper, we extended the existing text cube model to incorporate TF-IDF (Term Frequency Inverse Document Frequrency) and LM (Language Model) as measurements. Because the proposed text cube model utilizes new measurements which are more popular in information retrieval systems, it is more efficient and effective to analysis text databases. Through experiments, we revealed that the performance and the effectiveness of the proposed text cube outperform the existing one.