@article{saidani_supervised_2017,

title = {A supervised approach for spam detection using text-based semantic representation},

author = {N. Saidani and K. Adi and M. S. Allili},

editor = {Aimeur E. Weiss M. Ruhi U.},

url = {https://www.scopus.com/inward/record.uri?eid=2-s2.0-85019905686&doi=10.1007%2f978-3-319-59041-7_8&partnerID=40&md5=f416f274d5e08603fa6d1ec9a4cf9c43},

doi = {10.1007/978-3-319-59041-7_8},

issn = {18651348},

year  = {2017},

date = {2017-01-01},

journal = {Lecture Notes in Business Information Processing},

volume = {289},

pages = {136–148},

abstract = {In this paper, we propose an approach for email spam detection based on text semantic analysis at two levels. The first level allows categorization of emails by specific domains (e.g., health, education, finance, etc.). The second level uses semantic features for spam detection in each specific domain. We show that the proposed method provides an efficient representation of internal semantic structure of email content which allows for more precise and interpretable spam filtering results compared to existing methods. © Springer International Publishing AG 2017.},

note = {ISBN: 9783319590400 

Publisher: Springer Verlag},

keywords = {Domain categorization, E-mail spam, Electronic mail, Feature extraction, Semantic analysis, Semantic features, Semantic representation, Semantic structures, Semantics, Spam detection, Spam filtering},

pubstate = {published},

tppubtype = {article}

}