2016/06/17 by Noura Al Moubayed, Toby P. Breckon, Moubayed, Noura Al +5
Computer Science · #Computation and Language (cs.CL) #FOS: Computer and information sciences #Machine Learning (cs.LG) #Neural and Evolutionary Computing (cs.NE) #Sentiment Analysis and Opinion Mining #Spam and Phishing Detection #Text and Document Classification Technologies
paper · pdf · doi:10.48550/arxiv.1606.05554
openalex publication_date 2016/06/17 · openalex created_date 2022/09/28 · openalex updated_date 2026/07/28
In This paper we present a novel approach to spam filtering and demonstrate its applicability with respect to SMS messages. Our approach requires minimum features engineering and a small set of la- belled data samples. Features are extracted using topic modelling based on latent Dirichlet allocation, and then a comprehensive data model is created using a Stacked Denoising Autoencoder (SDA). Topic modelling summarises the data providing ease of use and high interpretability by visualising the topics using word clouds. Given that the SMS messages can be regarded as either spam (unwanted) or ham (wanted), the SDA is able to model the messages and accurately discriminate between the two classes without the need for a pre-labelled training set. The results are compared against the state-of-the-art spam detection algorithms with our proposed approach achieving over 97% accuracy which compares favourably to the best reported algorithms presented in the literature.