EMAIL SPAM DETECTION USING SUPPORT VECTOR MACHINES AND NATURAL LANGUAGE PROCESSING

The growing use of email communication has led to the massive influx of unwanted and viral
communications otherwise known as spam. Spamming must be detected effectively to make
sure that an email is not lost and that the email is secure. In this paper, an automated email
spam solution has been introduced and it is based on Natural Language Processing (NLP) and
a Multi- Kernel Support Vector Model (MK-SVM) model. Term Frequency Inverse Document
Frequency (TF-IDF) technique is used to convert email text into numerical features and
Support Vector Machines with Linear, Radial Basis Function (RBF), Polynomial, and Sigmoid
kernels are used to do the classification. Each of the kernels is tested based on several metrics
which include accuracy, precision, recall, specificity and errors rate. The experimental results
indicate that the Linear and Sigmoid kernel SVMs are the most accurate in classification with
a minimum error of 0.011, as compared to RBF and Polynomial kernels. These results show
that the simplicity of kernel functions is better applied to large textual data of high dimensions
and it would be the most appropriate to email spam filters based on NLP.