Exploiting geotagged resources for spatial clustering on social network services. (20th September 2015)

• Record Type: Journal Article
• Authors: Jung, Jason J.
• Other Names: Simmhan Yogesh guestEditor.; Ramakrishnan Lavanya guestEditor.; Antoniu Gabriel guestEditor.; Goble Carole guestEditor.; Yu Yong guestEditor.; Mu Yi guestEditor.; Lu Rongxing guestEditor.; Ren Jian guestEditor.; Venticinque Salvatore guestEditor.; Camacho David guestEditor.
• Is Part Of: Concurrency and computation. Volume 28:Number 4(2016)
• Page Start: 1356
• Page End: 1367
• Publication Date: 2015-09-20
• Subjects: spatial folksonomy -- geotagged resources -- Naive Bayes -- multiple SVM -- classification; Parallel processing (Electronic computers) -- Periodicals; Parallel computers -- Periodicals; 004.35
• DOI: 10.1002/cpe.3634
• Languages: English
• ISSNs: 1532-0626

Abstract

Summary: Nowadays, it has become common for users to geotag resources on many online social networking services. However, a large amount of data exists on social network services without annotations of their geographical location. Thus, it would be useful to tag these resources with geotags. This paper proposes a method to predict the location of unlabeled resources on social networking services. We use the Naive Bayes and support vector machine methods to classify the resources that are collected by using the term frequency of the tags in each class. In addition, we improve the calculation for these methods by using the values of the term frequency, and we invert the class frequency to optimize the input data. These results can be applied to tag unlabeled resources on social networking services. Copyright © 2015 John Wiley & Sons, Ltd.