EMFET: E-mail Features Extraction Tool
This tool addresses the need for efficient feature extraction in email analysis for practitioners and researchers, but it is incremental as it builds on existing methods without new algorithmic breakthroughs.
The paper introduces EMFET, an open-source tool for extracting 140 features from email corpora in EML format, categorized into header, payload, and attachment features, to aid in building datasets for spam detection models.
EMFET is an open source and flexible tool that can be used to extract a large number of features from any email corpus with emails saved in EML format. The extracted features can be categorized into three main groups: header features, payload (body) features, and attachment features. The purpose of the tool is to help practitioners and researchers to build datasets that can be used for training machine learning models for spam detection. So far, 140 features can be extracted using EMFET. EMFET is extensible and easy to use. The source code of EMFET is publicly available at GitHub (https://github.com/WadeaHijjawi/EmailFeaturesExtraction)