| Title | Word Embedding Method of SMS Messages for Spam Message Filtering |
| Publication Type | Conference Paper |
| Year of Publication | 2019 |
| Authors | Lee, Hyun-Young, Kang, Seung-Shik |
| Conference Name | 2019 IEEE International Conference on Big Data and Smart Computing (BigComp) |
| Date Published | feb |
| Keywords | binary classification, CBOW, classification methods, Deep Learning, deep learning method, electronic messaging, feature extraction, feedforward neural nets, feedforward neural network, Feedforward neural networks, Filtering, Human Behavior, information filtering, learning (artificial intelligence), machine learning method, natural language processing, pattern classification, popular machine learning method, pubcrawl, Resiliency, Scalability, security of data, SMS Filtering, SMS messages, spam message filtering, Support vector machines, SVM, SVM light, unsolicited e-mail, word embedding, word embedding method, word embedding technique, Word Vector |
| Abstract | SVM has been one of the most popular machine learning method for the binary classification such as sentiment analysis and spam message filtering. We explored a word embedding method for the construction of a feature vector and the deep learning method for the binary classification. CBOW is used as a word embedding technique and feedforward neural network is applied to classify SMS messages into ham or spam. The accuracy of the two classification methods of SVM and neural network are compared for the binary classification. The experimental result shows that the accuracy of deep learning method is better than the conventional machine learning method of SVM-light in the binary classification. |
| DOI | 10.1109/BIGCOMP.2019.8679476 |
| Citation Key | lee_word_2019 |
Word Embedding Method of SMS Messages for Spam Message Filtering