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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.<\/jats:p>","DOI":"10.4018\/ijbdcn.2017070103","type":"journal-article","created":{"date-parts":[[2017,5,3]],"date-time":"2017-05-03T16:20:31Z","timestamp":1493828431000},"page":"28-40","source":"Crossref","is-referenced-by-count":1,"title":["Bi-Term Topic Model for SMS Classification"],"prefix":"10.4018","volume":"13","author":[{"given":"Jialin","family":"Ma","sequence":"first","affiliation":[{"name":"The Laboratory for Internet of Things and Mobile Internet Technology of Jiangsu Province, Huaiyin Institute of Technology, Huaian, China & College of Computer and Information, Hohai University, Nanjing, China"}]},{"given":"Yongjun","family":"Zhang","sequence":"additional","affiliation":[{"name":"Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, China & College of Computer and Information, Hohai University, Nanjing, China"}]},{"given":"Lin","family":"Zhang","sequence":"additional","affiliation":[{"name":"Faculty of Management Engineering, Huaiyin Institute of Technology, Huaian, China"}]},{"given":"Kun","family":"Yu","sequence":"additional","affiliation":[{"name":"Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, China"}]},{"given":"Jinlin","family":"Liu","sequence":"additional","affiliation":[{"name":"Faculty of Computer and Software Engineering, Huaiyin Institute of Technology, Huaian, China"}]}],"member":"2432","reference":[{"key":"ijbdcn.2017070103-0","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2014.08.054"},{"key":"ijbdcn.2017070103-1","doi-asserted-by":"publisher","DOI":"10.1145\/2034691.2034742"},{"key":"ijbdcn.2017070103-2","doi-asserted-by":"publisher","DOI":"10.1504\/IJSSC.2011.039104"},{"key":"ijbdcn.2017070103-3","doi-asserted-by":"publisher","DOI":"10.1504\/IJGUC.2014.058244"},{"key":"ijbdcn.2017070103-4","doi-asserted-by":"publisher","DOI":"10.1145\/2133806.2133826"},{"key":"ijbdcn.2017070103-5","unstructured":"Blei, D.M., Ng, A.Y., & Jordan, M.I. 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