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Does Sentiment Analysis Help in Bayesian Spam Filtering?

  • Enaitz EzpeletaEmail author
  • Urko Zurutuza
  • José María Gómez Hidalgo
Conference paper
Part of the Lecture Notes in Computer Science book series (LNCS, volume 9648)

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.

Keywords

Spam Polarity Security Bayes Sentiment analysis 

Notes

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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Copyright information

© Springer International Publishing Switzerland 2016

Authors and Affiliations

  • Enaitz Ezpeleta
    • 1
    Email author
  • Urko Zurutuza
    • 1
  • José María Gómez Hidalgo
    • 2
  1. 1.Electronics and Computing DepartmentMondragon UniversityArrasate-MondragónSpain
  2. 2.Pragsis TechnologiesMadridSpain

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