A Spam Transformer Model for SMS Spam Detection
type: CreativeWork
description: With the prosperity of the Short Message Service (SMS), the increasing number of spam messages has become a serious problem. The need to block spam messages requires us to develop new SMS spam detection technologies. The Transformer, an attention- based sequence to sequence model, has achieved excellent results in multiple different tasks recently. In this thesis, we propose a modified Transformer model for SMS spam messages detection. The evaluation of our proposed modified spam Transformer is performed on SMS Spam Collection v.1 dataset and UtkMl’s Twitter Spam Detection Competition dataset, with the benchmark of multiple established classifiers such as Logistic Regression, Na ̈ıve Bayes, Random Forests, Support Vector Machine, and Long Short-Term Memory. In comparison to all other candidates, our experiments show that the proposed modified spam Transformer achieves the best results in terms of almost all selected performance criteria.
abstract: With the prosperity of the Short Message Service (SMS), the increasing number of spam messages has become a serious problem. The need to block spam messages requires us to develop new SMS spam detection technologies. The Transformer, an attention- based sequence to sequence model, has achieved excellent results in multiple different tasks recently. In this thesis, we propose a modified Transformer model for SMS spam messages detection. The evaluation of our proposed modified spam Transformer is performed on SMS Spam Collection v.1 dataset and UtkMl’s Twitter Spam Detection Competition dataset, with the benchmark of multiple established classifiers such as Logistic Regression, Na ̈ıve Bayes, Random Forests, Support Vector Machine, and Long Short-Term Memory. In comparison to all other candidates, our experiments show that the proposed modified spam Transformer achieves the best results in terms of almost all selected performance criteria.
author: Liu, Xiaoxu
language: en
keywords: SMS spam detection, Transformer, Attention, Deep learning
date published: 2021-04-29T17:43:23Z
date modified: 2026-07-24T03:39:32Z
provider: University of Ottawa
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: Université d'Ottawa / University of Ottawa

PropertyValue
property ID: Handle
value: http://hdl.handle.net/10393/42057

PropertyValue
property ID: DOI
value: 10.20381/ruor-26279