Abstract
At present, Short Message Service (SMS) is widespread in many countries. Many researchers usually use conventional text classifiers to filter SMS spam. In fact, the actual situation of SMS spam messages isn’t consideration by most reseachers. Because the obvious characteristic is the content of SMS spam messages are miscellaneous, shorter and variant. Therefore, traditional classifiers aren’t fit to use for SMS spam filtering directly. In this paper, we propose to utilize A Biterm Topic Model(BTM) to identify SMS spam. The BTM can effectively learn latent semantic features from SMS spam corpus. The experiments in our work show the BTM can learn higher quality of topic features from SMS spam corpus, and can more effective in the task of SMS spam filtering.
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Ma, J., Zhang, Y., Zhang, L. (2017). Mobile Spam Filtering base on BTM Topic Model. In: Xhafa, F., Barolli, L., Amato, F. (eds) Advances on P2P, Parallel, Grid, Cloud and Internet Computing. 3PGCIC 2016. Lecture Notes on Data Engineering and Communications Technologies, vol 1. Springer, Cham. https://doi.org/10.1007/978-3-319-49109-7_63
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DOI: https://doi.org/10.1007/978-3-319-49109-7_63
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