Rule-Based Topic Trend Analysis by Using Data Mining Techniques

Yunwan Jeon, Chanho Cho, Jongwoo Seo, Kyunglag Kwon, Hansaem Park, In-Jeong Chung

Abstract: Many users in social web environments share and publish user-generated contents such as tastes, opinions, and ideas in the form of text and multimedia data. Various research studies have been conducted on the analysis of such social data, which can be used for discovering users’ thoughts on specific topics. But, there are still challenging tasks to find out the meaningful patterns from the social data due to rapidly increasing amount of data. In this paper, we therefore propose a rule-based topic trend analysis by using On-Line-Analytical Processing (OLAP) and Association Rule Mining (ARM) to detect information such as previously unknown or abnormal events or situations. For the verification of the proposed method, we conduct experiments to demonstrate that the method is feasible to perform rule-based topic trend analysis.