Annotating semantic tags of locations in location-based social networks

Location-based social networks (LBSNs) have become popular platforms that allow users to share their check-in activities with friends. Annotating semantic tags of locations, as one of the hottest research topics in LBSNs, has attracted considerable attention. Semantic annotation requires sufficient location features to train classifiers. Based on the analysis of LBSN data, we find that users’ check-in activities have similarities that can promote the extraction of location features and improve the accuracy of semantic annotation. However, the existing studies ignored the use of the similarities of users’ check-in activities for extracting suitable location features. Therefore, in this paper, a new location feature, called the similar user pattern (SUP), is first extracted by capturing the similarities among the different users’ check-in activities. Second, annotating semantic tags of locations is treated as a multi-label classification problem. Thus, multi-label semantic annotation with an extreme learning machine (ELM) is proposed, called MSA-ELM. The MSA-ELM algorithm trains a binary ELM classifier for each tag in the tag space to support multi-label classification. Finally, a series of experiments are conducted to demonstrate both the accuracy and efficiency of annotating semantic tags of locations. The experimental results show that the SUP is a proper location feature, and the MSA-ELM algorithm has a good performance for multi-label semantic annotation.