An Automatic Annotation Method for Discovering Semantic Information of Geographical Locations from Location-Based Social Networks
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College of Computer, Nanjing University of Posts and Telecommunications, Nanjing 210023, China
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Jiangsu Key Laboratory of Big Data Security and Intelligent Processing, Nanjing 210023, China
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Jiangsu Center for Collaborative Innovation in Geographical Information Resource Development and Application, School of Geography, Nanjing Normal University, Nanjing 210023, China
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State Key Laboratory of Resources and Environmental Information System, Institute of Geographic Sciences and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, China
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Department of Geography, University of Wisconsin-Madison, Madison, WI 53706, USA
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Center for Social Sciences, Southern University of Science and Technology, Shenzhen 518055, China
*
Author to whom correspondence should be addressed.
ISPRS Int. J. Geo-Inf. 2019, 8(11), 487; https://doi.org/10.3390/ijgi8110487
Received: 14 September 2019 / Revised: 25 October 2019 / Accepted: 28 October 2019 / Published: 29 October 2019
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.
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Keywords:
location-based social networks; data mining; points of interest; Flickr; points of interest annotation
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MDPI and ACS Style
Zou, Z.; He, X.; Zhu, A.-X. An Automatic Annotation Method for Discovering Semantic Information of Geographical Locations from Location-Based Social Networks. ISPRS Int. J. Geo-Inf. 2019, 8, 487.
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