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Bi-Term Topic Model for SMS Classification

Bi-Term Topic Model for SMS Classification

Jialin Ma, Yongjun Zhang, Lin Zhang, Kun Yu, Jinlin Liu
Copyright: © 2017 |Volume: 13 |Issue: 2 |Pages: 13
ISSN: 1548-0631|EISSN: 1548-064X|EISBN13: 9781522511403|DOI: 10.4018/ijbdcn.2017070103
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MLA

Ma, Jialin, et al. "Bi-Term Topic Model for SMS Classification." IJBDCN vol.13, no.2 2017: pp.28-40. http://doi.org/10.4018/ijbdcn.2017070103

APA

Ma, J., Zhang, Y., Zhang, L., Yu, K., & Liu, J. (2017). Bi-Term Topic Model for SMS Classification. International Journal of Business Data Communications and Networking (IJBDCN), 13(2), 28-40. http://doi.org/10.4018/ijbdcn.2017070103

Chicago

Ma, Jialin, et al. "Bi-Term Topic Model for SMS Classification," International Journal of Business Data Communications and Networking (IJBDCN) 13, no.2: 28-40. http://doi.org/10.4018/ijbdcn.2017070103

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Abstract

With the overflowing of Short Message Service (SMS) spam nowadays, many traditional text classification algorithms are used for SMS spam filtering. Nevertheless, because the content of SMS spam messages are miscellaneous and distinct from general text files, such as more shorter, usually including mass of abbreviations, symbols, variant words and distort or deform sentences, the traditional classifiers aren't fit for the task of SMS spam filtering. In this paper, the authors propose a Short Message Biterm Topic Model (SM-BTM) which can be used to automatically learn latent semantic features from SMS spam corpus for the task of SMS spam filtering. The SM-BTM is based on the probability of topic model theory and Biterm Topic Model (BTM). The experiments in this work show the proposed model SM-BTM can acquire higher quality of topic features than the original BTM, and is more suitable for identifying the miscellaneous SMS spam.

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