Title: Content-based Approach for Vietnamese Spam SMS Filtering,
Abstract: Short Message Service (SMS) spam is a serious problem in Vietnam because of
the availability of very cheap pre-paid SMS packages. There are some systems to
detect and filter spam messages for English, most of which use machine learning
techniques to analyze the content of messages and classify them. For
Vietnamese, there is some research on spam email filtering but none focused on
SMS. In this work, we propose the first system for filtering Vietnamese spam
SMS. We first propose an appropriate preprocessing method since existing tools
for Vietnamese preprocessing cannot give good accuracy on our dataset. We then
experiment with vector representations and classifiers to find the best model
for this problem. Our system achieves an accuracy of 94% when labelling spam
messages while the misclassification rate of legitimate messages is relatively
small, about only 0.4%. This is an encouraging result compared to that of
English and can be served as a strong baseline for future development of
Vietnamese SMS spam prevention systems.