The recent popularity of location-based social (LBS) networking services has resulted in huge volumes of geo-tagged data from social media, allowing us to monitor massive lifelogs from a real-world space. Also, the characteristics of urban areas, placeness, were identified from the lifelogs attained. Based on this concern, in this paper, we propose a new approach of placeness extraction with an ontology-based urban area placeness identification system. The suggested technique uses the textual, temporal, and spatial information of a LBS post from a specific area, and combines this information with the help of ontology. This combination measures the areas occasion-oriented placeness, which can be subdivided into time or companions. Our work focuses on a case study of Twitter data from the city of Seoul. The results show that our system is able to extract subdividable placeness and suitable correspondences when compared to real world socio-geographic features.