Seo Yoon Jang  Ji Hoon Kang

10.5626/JOK.2020.47.9.814

intelligent services contextual services Social Media Analytics User Interest

By analyzing social media (SNS) data with learning algorithms that can be obtained from social networks, it is possible to extract the information of personal or social concerns. The learning algorithms have a problem, however, in that the higher accuracy from analysis requires larger volumes of information but thus increases the analysis duration. To resolve this issue effectively, we propose a system, termed Location Information Sharing Objects (LISO). The LISO has two types of objects. The LISO learns from big data by classifying the role of the objects for analyzing social concerns based on their type. The fixed-position objects manage collecting and analyzing a wide range of location-specific social media data for obtaining social concerns. The mobile objects manage analyzing the information regarding frequently varying situations as well as users’ personal concerns. This role-sharing method for analyzing big social media data based on the type of the objects in the LISO can distribute the load of analyzing.