An Automatic Annotation Method for Discovering Semantic Information of Geographical Locations from Location-Based Social Networks

Zou Zou,He He,Zhu Zhu

doi:10.3390/ijgi8110487

Abstract

Location-Based Social Networks (LBSNs) contain rich information that can be used to identify and annotate points of interest (POIs). Discovering these POIs and annotating them with this information is not only helpful for understanding the social behavior of users, but it also provides benefits for location recommendations. However, current methods still have some limitations, such as a long annotating time and a low annotating accuracy. In this study, we develop a hybrid method to annotate POIs with meaningful information from LBSNs. The method integrates three patterns: temporal, spatial, and text patterns. Firstly, we present an approach for preprocessing data based on temporal patterns. Secondly, we describe a way to discover POIs through spatial patterns. Thirdly, we build a keyword dictionary for discovering the categories of POIs to be annotated via mining the text patterns. Finally, we integrate these three patterns to label each POI. Taking New York and London as the target areas, we accomplish automatic POI annotation by using Precision, Recall, and F-values to evaluate the effectiveness. The results show that our F-value is 78%, which is superior to that of the baseline method (Falcone's method) at 73% and this suggests that our method is effective in extracting POIs and assigning them categories.

Highlights

With the rapid expansion in the use of smartphones and the advancements in positioning technology, a large volume of data from Location-Based Social Networks (LBSNs), such as Foursquare, Flickr, and Facebook Places, have been produced [1,2,3,4,5]
Current methods of automatic Points of Interest (POIs) annotation can be classified into two main types according to the information patterns used: single pattern-based methods and hybrid pattern-based methods
In the study region of New York, the clusters with a high density and large area were concentrated on Manhattan Island, while small-sized and medium-sized clusters were mainly located on the marginal areas of the island and the other side of the rivers

Summary

Introduction

With the rapid expansion in the use of smartphones and the advancements in positioning technology, a large volume of data from Location-Based Social Networks (LBSNs), such as Foursquare, Flickr, and Facebook Places, have been produced [1,2,3,4,5]. These LBSN data contain an enormous amount of check-in records that contain information on location (Latitude/longitude) and time (timestamp) as well as comments. Hybrid pattern-based methods often combine multiple patterns, such as spatiotemporal pattern-based methods, to perform POI annotation [9,10,11]

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Methods

Results

Discussion

Conclusion