Abstract
About more than half of worldwide email traffic, amounting several billions per day consists of spam causing considerable disturbance in telecommunications. This upheaval volume of unwanted messages implies an intense need for reliable and robust spam filters. Conventional filtering methods have largely failed to tackle the adaptive nature of spam messages. Machine learning methods, on the contrary, may have the ability to intelligently detect and filter spams. Here, we present a system of spam message prediction based on appropriate lexical analysis like tokenization, stop-words removal, stemming, lemmatization, and feature extraction. Impressive results, i.e., over 97% accuracy with random forest classifier, have been obtained in several experiments on the UCI spam collection dataset. We have also hosted the developed spam-detection system on Heroku as platform-as-a-service (PAAS).
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Sartaj, S., Mollah, A.F. (2021). An Intelligent System for Spam Message Detection. In: Sheth, A., Sinhal, A., Shrivastava, A., Pandey, A.K. (eds) Intelligent Systems. Algorithms for Intelligent Systems. Springer, Singapore. https://doi.org/10.1007/978-981-16-2248-9_37
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DOI: https://doi.org/10.1007/978-981-16-2248-9_37
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