| Title | Convolutional Neural Network Based SMS Spam Detection |
| Publication Type | Conference Paper |
| Year of Publication | 2018 |
| Authors | Popovac, M., Karanovic, M., Sladojevic, S., Arsenovic, M., Anderla, A. |
| Conference Name | 2018 26th Telecommunications Forum (℡FOR) |
| Date Published | nov |
| Keywords | anti-spam filters, Classification algorithms, CNN, convolutional neural nets, convolutional neural network, convolutional neural networks, Cost-sensitive classification, Data preprocessing, electronic messaging, feature extraction, Human Behavior, Imbalanced dataset, learning (artificial intelligence), machine learning, Metrics, pubcrawl, Scalability, SMS Spam, SMS spam detection, spam detection, spam messages, Support vector machines, text analysis, text categorization, Tiago dataset, undesired text message, unsolicited e-mail, Unsolicited electronic mail |
| Abstract | 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. |
| DOI | 10.1109/℡FOR.2018.8611916 |
| Citation Key | popovac_convolutional_2018 |
Convolutional Neural Network Based SMS Spam Detection