{"about":{"site":"https://codewithpapers.app","non_affiliation":"Code with Papers and Syntology are not affiliated with, endorsed by, or sponsored by Papers with Code, Meta, or the pwc-archive mirror.","licence":"CC BY-SA 4.0","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","attribution":"https://codewithpapers.app/attribution","modified":"archive material modified by Syntology; see the attribution page"},"url":"/paper/emfet-e-mail-features-extraction-tool","title":"EMFET: E-mail Features Extraction Tool","arxiv_id":"1711.08521","date":"2017-11-22","proceeding":null,"authors":["Wadi' Hijawi","Hossam Faris","Ja'far Alqatawna","Ibrahim Aljarah","Ala' M. Al-Zoubi","Maria Habib"],"abstract":"EMFET is an open source and flexible tool that can be used to extract a large\nnumber of features from any email corpus with emails saved in EML format. The\nextracted features can be categorized into three main groups: header features,\npayload (body) features, and attachment features. The purpose of the tool is to\nhelp practitioners and researchers to build datasets that can be used for\ntraining machine learning models for spam detection. So far, 140 features can\nbe extracted using EMFET. EMFET is extensible and easy to use. The source code\nof EMFET is publicly available at GitHub\n(https://github.com/WadeaHijjawi/EmailFeaturesExtraction)","url_abs":"http://arxiv.org/abs/1711.08521v1","url_pdf":"http://arxiv.org/pdf/1711.08521v1.pdf","source":{"archive":"pwc-archive (Hugging Face), CC BY-SA 4.0","snapshot":"2025-07-28","licence_url":"https://creativecommons.org/licenses/by-sa/4.0/legalcode","row_kind":"abstracts"},"code_links":[{"paper_slug":"emfet-e-mail-features-extraction-tool","repo_url":"https://github.com/WadeaHijjawi/EmailFeaturesExtraction","is_official":1,"mentioned_in_paper":1,"mentioned_in_github":0,"framework":"none","reach":null}],"tasks":[{"task_slug":"machine-learning","task_name":"BIG-bench Machine Learning"},{"task_slug":"spam-detection","task_name":"Spam detection"}],"methods":[],"datasets_introduced":[],"methods_introduced":[],"results":[],"syntology":{"atlas_url":null,"mcp":null,"developers":"https://syntology.ai/developers"},"arxiv_metadata":null,"syntology_extracted_results":null}