A feature-centric spam email detection model using diverse supervised machine learning algorithms

Abstract

Purpose This research study proposes a feature-centric spam email detection model (FSEDM) based on content, sentiment, semantic, user and spam-lexicon features set. The purpose of this study is to exploit the role of sentiment features along with other proposed features to evaluate the classification accuracy of machine learning algorithms for spam email detection. Design/methodology/approach Existing studies primarily exploits content-based feature engineering approach; however, a limited number of features is considered. In this regard, this research study proposed a feature-centric framework (FSEDM) based on existing and novel features of email data set, which are extracted after pre-processing. Afterwards, diverse supervised learning techniques are applied on the proposed features in conjunction with feature selection techniques such as information gain, gain ratio and Relief-F to rank most prominent features and classify the emails into spam or ham (not spam). Findings Analysis and experimental results indicated that the proposed model with sentiment analysis is competitive approach for spam email detection. Using the proposed model, deep neural network applied with sentiment features outperformed other classifiers in terms of classification accuracy up to 97.2%. Originality/value This research is novel in this regard that no previous research focuses on sentiment analysis in conjunction with other email features for detection of spam emails.