Abstract
The paper aims to present a Spam Detection system by a Content Analysis based on Machine Leaning. The system is composed of six units: Tokenization and Cleaning words, Lemmatization, Stopping Word Removal and Synonym Replacement, Term Selection, Bag-of-Words Representer, and Classifier. Experiments performed on two different datasets, i.e., SpamAssassin and Trec2007 show satisfactory results, comparable with the state of the art.
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Acknowledgments
Daniele Davino developed part of the work, as exam project for Multimedia Systems and Laboratory, during his M.Sc. in Computer Science at University of Naples Parthenope, under the supervision of Francesco Camastra and Angelo Ciaramella. Francesco Camastra’s, Angelo Ciaramella’s, and Antonino Staiano’s researches were funded by Sostegno alla ricerca individuale per il triennio 2015–17 project of University of Naples Parthenope.
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Davino, D., Camastra, F., Ciaramella, A., Staiano, A. (2021). Spam Detection by Machine Learning-Based Content Analysis. In: Esposito, A., Faundez-Zanuy, M., Morabito, F., Pasero, E. (eds) Progresses in Artificial Intelligence and Neural Systems. Smart Innovation, Systems and Technologies, vol 184. Springer, Singapore. https://doi.org/10.1007/978-981-15-5093-5_37
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DOI: https://doi.org/10.1007/978-981-15-5093-5_37
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