Biblio

SMS spam refers to undesired text message. Machine Learning methods for anti-spam filters have been noticeably effective in categorizing spam messages. Dataset used in this research is known as Tiago's dataset. Crucial step in the experiment was data preprocessing, which involved reducing text to lower case, tokenization, removing stopwords. Convolutional Neural Network was the proposed method for classification. Overall model's accuracy was 98.4%. Obtained model can be used as a tool in many applications.

Short Message Service is now-days the most used way of communication in the electronic world. While many researches exist on the email spam detection, we haven't had the insight knowledge about the spam done within the SMS's. This might be because the frequency of spam in these short messages is quite low than the emails. This paper presents different ways of analyzing spam for SMS and a new pre-processing way to get the actual dataset of spam messages. This dataset was then used on different algorithm techniques to find the best working algorithm in terms of both accuracy and recall. Random Forest algorithm was then implemented in a real world application library written in C\# for cross platform .Net development. This library is capable of using a prebuild model for classifying a new dataset for spam and ham.