Abstract
Social networking services (SNS) have been an important sources of geotagged resources. This paper proposes Naive Bayes method-based framework to predict the locations of non-geotagged resources on SNS. By computing TF-ICF weights (Term Frequency and Inverse Class Frequency) of tags, we discover meaningful associations between the tags and the classes (which refer to sets of locations of the resources). As the experimental result, we found that the proposed method has shown around 75% of accuracy, with respect to F1 measurement.
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Nguyen, T.T., Hwang, D., Jung, J.J. (2015). Social Tagging Analytics for Processing Unlabeled Resources:A Case Study on Non-geotagged Photos. In: Camacho, D., Braubach, L., Venticinque, S., Badica, C. (eds) Intelligent Distributed Computing VIII. Studies in Computational Intelligence, vol 570. Springer, Cham. https://doi.org/10.1007/978-3-319-10422-5_37
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DOI: https://doi.org/10.1007/978-3-319-10422-5_37
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-10421-8
Online ISBN: 978-3-319-10422-5
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