Deep learning based spam detection system

Abstract

In this paper, we propose a deep learning-based spam detection model. This model is a combination of the Word Embedding technique and Neural Network algorithm. Word Embedding allows a distributed representation of words in the feature space where word's meaning and word analogy can be represented. Deep neural network is used to learn features of text documents represented in the embedding space and use these features to classify text documents. This model architecture is expected to be able to effectively detect spams in various types of text documents as well as in large document corpus.