Department of Computer and Radio Communications Engineering, Korea University, Seoul, Republic of Korea
Department of Computer Science & Engineering, Sun Moon University, Asan, Republic of Korea.
Graduate School of Convergence IT, Korea University, Seoul, Republic of Korea
In a Social Network Service (SNS), a large amount of data with a variety of characteristics is generated through voluntary participation of users. These data are called Big Social Data." Big social data can identify not only content registered on the web but also the relations of the friends of users. One of the most representative studies on SNS is analysis of the characteristics of social content and social relations, because SNS users tend to add people who are in close contact with them and have similar interests to their list of friends. Finding new knowledge from these large amounts of big social data can be very useful. This paper proposes a polarity analysis method for discovering hidden knowledge based on formal concept analysis in SNSs called PA-DHK. Further, we show, via experiments, that our data analysis approach can be applied to knowledge discovery using association rules.