SMS spam filtering using probabilistic topic modelling and Stacked Denoising Autoencoder.

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 labelled 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

SMS Spam Filtering using Probabilistic Topic Modelling and Stacked Denoising Autoencoder

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 labelled 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

Evaluating latent content within unstructured text: an analytical methodology based on a temporal network of associated topics

Abstract In this research various concepts from network theory and topic modelling are combined, to provision a temporal network of associated topics. This solution is presented as a step-by-step process to facilitate the evaluation of latent topics from unstructured text, as well as the domain area that textual documents are sourced from. In addition to ensuring shifts and changes in the structural properties of a given corpus are visible, non-stationary classes of cooccurring topics are determined, and trends in topic prevalence, positioning, and association patterns are evaluated over time. The aforementioned capabilities extend the insights fostered from stand-alone topic modelling outputs, by ensuring latent topics are not only identified and summarized, but more systematically interpreted, analysed, and explained, in a transparent and reliable way