期刊
CONCURRENCY AND COMPUTATION-PRACTICE & EXPERIENCE
卷 28, 期 4, 页码 1356-1367出版社
WILEY
DOI: 10.1002/cpe.3634
关键词
spatial folksonomy; geotagged resources; Naive Bayes; multiple SVM; classification
资金
- MSIP (Ministry of Science, ICT & Future Planning), Korea, under the ITRC (Information Technology Research Center) [NIPA-2014-H0301-14-1044]
- National Research Foundation of Korea (NRF) - Korea government (MSIP) [NRF-2014R1A2A2A05007154]
- National Research Foundation of Korea [2014R1A2A2A05007154] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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 (c) 2015 John Wiley & Sons, Ltd.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据