A feature-centric spam email detection model using diverse supervised machine learning algorithms.
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 classificati...
| Publicado en: | Electronic Library Vol. 38; no. 3; pp. 633 - 658 |
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| Autores principales: | , , , , |
| Formato: | computer program equations & formulas research tables/charts Journal Article |
| Publicado: |
Emerald Publishing Limited
2020
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=144687107&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 144687107 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 02640473 2OJ jtl: Electronic Library issn: 02640473 maglogo: N pubinfo: dt: 2020 vid: 38 iid: 3 pid: 465 pub: Emerald Publishing Limited artinfo: ui: 144687107 144687107 144687107 10.1108/EL-07-2019-0181 144687107 ppf: 633 ppct: 25 formats: tig: atl: A feature-centric spam email detection model using diverse supervised machine learning algorithms. aug: au: Zamir, Ammara Khan, Hikmat Ullah Mehmood, Waqar Iqbal, Tassawar Akram, Abubakker Usman affil: Department of Computer Science, COMSATS University Islamabad, Wah Campus, Wah Cantt, Pakistan sug: subj: Email Classification Machine Learning Methods Algorithms Emotions Semantics Models, Statistical Human Conceptual Framework Neural Networks (Computer) Descriptive Statistics User-Computer Interface Language Processing Software Design ab: 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. pubtype: Academic Journal doctype: computer program equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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