Abstract
Unsolicited email campaigns remain as one of the biggest threats affecting millions of users per day. During the last years several techniques to detect unsolicited emails have been developed. Among all proposed automatic classification techniques, machine learning algorithms have achieved more success, obtaining detection rates up to a 96 % [1]. This work provides means to validate the assumption that being spam a commercial communication, the semantics of its contents are usually shaped with a positive meaning. We produce the polarity score of each message using sentiment classifiers, and then we compare spam filtering classifiers with and without the polarity score in terms of accuracy. This work shows that the top 10 results of Bayesian filtering classifiers have been improved, reaching to a 99.21 % of accuracy.
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Acknowledgments
This work has been partially funded by the Basque Department of Education, Language policy and Culture under the project SocialSPAM (PI_2014_1_102).
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Ezpeleta, E., Zurutuza, U., Gómez Hidalgo, J.M. (2016). Does Sentiment Analysis Help in Bayesian Spam Filtering?. In: Martínez-Álvarez, F., Troncoso, A., Quintián, H., Corchado, E. (eds) Hybrid Artificial Intelligent Systems. HAIS 2016. Lecture Notes in Computer Science(), vol 9648. Springer, Cham. https://doi.org/10.1007/978-3-319-32034-2_7
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DOI: https://doi.org/10.1007/978-3-319-32034-2_7
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