Universiti Teknologi Malaysia Institutional Repository

SMS spam classification using vector space model and artificial neural network

Amir Sjarif, Nilam Nur and Ya'Acob, Suraya and Mohd. Yusoff, Rasimah Che and Mohd. Azmi, Nurulhuda Firdaus and Yuhaniz, Siti Sophiayati and Wan Safie, Wan Nazirul Hafeez (2018) SMS spam classification using vector space model and artificial neural network. In: 4th International Workshop on Big Data Analytics 2018.

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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

Item Type:Conference or Workshop Item (Paper)
Subjects:T Technology > T Technology (General)
Divisions:Razak School of Engineering and Advanced Technology
ID Code:83270
Deposited By: Siti Nor Hashidah Zakaria
Deposited On:30 Sep 2019 13:16
Last Modified:20 Oct 2019 01:58

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