SMS spam classification using vector space model and artificial neural network
Nilam Nur Amir Sjarif, Suraya Yaacob, Rasimah Che Mohd Yusoff, Nurulhuda Firdaus Mohd Azmi, Siti Sophiayati Yuhaniz, Wan Nazirul Hafeez Wan Safie · 2018
As there are increasing numbers of mobile subscriber and the market demands of reaching customer personally, Short Message Service (SMS) has become a target of unsolicited text message known as Spam that resulting waste in time, money, and privacy. Many text classification methods using traditional machine learning algorithm has been proposed to prevent spam. However, none of these solutions can guarantee 100% spam-proof solution as each filtering and modeling technique has their own weaknesses and strengths. The objective of this paper is to propose SMS spam classification using Vector Space Model (VSM) and Artificial Neural Network techniques on the publicly available SMS dataset. The result shows a significant improvement based on the accuracy which is 99.10%. This paper will contribute on practical applications and provide contribution to the body of knowledge