TY - JFULL AU - Jatinderkumar R. Saini and Apurva A. Desai PY - 2011/9/ TI - Identification of Non-Lexicon Non-Slang Unigrams in Body-enhancement Medicinal UBE T2 - International Journal of Computer and Information Engineering SP - 972 EP - 979 VL - 5 SN - 1307-6892 UR - https://publications.waset.org/pdf/15887 PU - World Academy of Science, Engineering and Technology NX - Open Science Index 56, 2011 N2 - Email has become a fast and cheap means of online communication. The main threat to email is Unsolicited Bulk Email (UBE), commonly called spam email. The current work aims at identification of unigrams in more than 2700 UBE that advertise body-enhancement drugs. The identification is based on the requirement that the unigram is neither present in dictionary, nor is a slang term. The motives of the paper are many fold. This is an attempt to analyze spamming behaviour and employment of wordmutation technique. On the side-lines of the paper, we have attempted to better understand the spam, the slang and their interplay. The problem has been addressed by employing Tokenization technique and Unigram BOW model. We found that the non-lexicon words constitute nearly 66% of total number of lexis of corpus whereas non-slang words constitute nearly 2.4% of non-lexicon words. Further, non-lexicon non-slang unigrams composed of 2 lexicon words, form more than 71% of the total number of such unigrams. To the best of our knowledge, this is the first attempt to analyze usage of non-lexicon non-slang unigrams in any kind of UBE. ER -