Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
EXTENDED SEARCH
Log in
Top
Published in:
Screening a Case Base for Stroke Disease Detection Read chapter Read first chapter

2016 | OriginalPaper | Chapter

Does Sentiment Analysis Help in Bayesian Spam Filtering?

Authors : Enaitz Ezpeleta, Urko Zurutuza, José María Gómez Hidalgo

Published in: Hybrid Artificial Intelligent Systems

Publisher: Springer International Publishing

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

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.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter An HMM-Based Multi-view Co-training Framework for Single-View Text Corpora
next chapter A Context-Aware Keyboard Generator for Smartphone Using Random Forest and Rule-Based System
Footnotes
Literature
1.
Malarvizhi, R.: Content-based spam filtering and detection algorithms-an efficient analysis & comparison 1 (2013)
2.
3.
Saadat, N.: Survey on spam filtering techniques. Commun. Netw. 3(3), 153–160 (2011)CrossRef
4.
Cormack, G.V.: Email spam filtering: a systematic review. Found. Trends Inf. Retrieval 1(4), 335–455 (2007)CrossRef
5.
Tretyakov, K.: Machine learning techniques in spam filtering. In: Data Mining Problem-oriented Seminar, MTAT, vol. 3, pp. 60–79 (2004)
6.
Sanz, E.P., Hidalgo, J.M.G., Cortizo, J.C.: Email spam filtering. Adv. Comput. 74, 45–114 (2008)CrossRef
7.
Teli, S., Biradar, S.: Effective spam detection method for email. In: International Conference on Advances in Engineering & Technology (2014)
8.
Eberhardt, J.J.: Bayesian spam detection. University of Minnesota, Morris Undergraduate Journal, Scholarly Horizons (2015)
9.
Liddy, E.: Natural language processing (2001)
10.
Giyanani, R., Desai, M.: Spam detection using natural language processing. Int. J. Comput. Sci. Res. Technol. 1, 55–58 (2013)
11.
Echeverria Briones, P.F., Altamirano Valarezo, Z.V., Pinto Astudillo, A.B., Sanchez Guerrero, J.D.C.: Text mining aplicado a la clasificación y distribución automática de correo electrónico y detección de correo spam (2009)
12.
Lau, R.Y.K., Liao, S.Y., Kwok, R.C.W., Xu, K., Xia, Y., Li, Y.: Text mining and probabilistic language modeling for online review spam detection. ACM Trans. Manage. Inf. Syst. 2(4), 25:1–25:30 (2012)
13.
Liu, B., Zhang, L.: A survey of opinion mining and sentiment analysis. In: Aggarwal, C.C., Zhai, C. (eds.) Mining Text Data, pp. 415–463. Springer, New York (2012)CrossRef
14.
Pang, B., Lee, L.: Opinion mining and sentiment analysis. Found. Trends Inf. Retrieval 2(1–2), 1–135 (2008)CrossRef
15.
Pang, B., Lee, L., Vaithyanathan, S.: Thumbs up?: sentiment classification using machine learning techniques. In: Proceedings of the ACL-02 Conference on Empirical Methods in Natural Language Processing, EMNLP 2002, Stroudsburg, PA, USA, vol. 10, pp. 79–86. Association for Computational Linguistics (2002)
16.
Turney, P.D.: Thumbs up or thumbs down?: semantic orientation applied to unsupervised classification of reviews. In: Proceedings of the 40th Annual Meeting on Association for Computational Linguistics, ACL 2002, pp. 417–424. Association for Computational Linguistics, USA (2002)
17.
Esuli, A., Sebastiani, F.: Sentiwordnet: a publicly available lexical resource for opinion mining. In: Proceedings of LREC, vol. 6, pp. 417–422. Citeseer (2006)
18.
Baccianella, S., Esuli, A., Sebastiani, F.: Sentiwordnet 3.0: an enhanced lexical resource for sentiment analysis and opinion mining. In: LREC, vol. 10, pp. 2200–2204 (2010)
19.
Ohana, B., Tierney, B.: Sentiment classification of reviews using sentiwordnet. In: 9th. IT & T Conference, p. 13 (2009)
20.
Pang, B., Lee, L.: A sentimental education: sentiment analysis using subjectivity summarization based on minimum cuts. In: Proceedings of the ACL (2004)
Metadata
Title
Does Sentiment Analysis Help in Bayesian Spam Filtering?
Authors
Enaitz Ezpeleta
Urko Zurutuza
José María Gómez Hidalgo
Copyright Year
2016
Book
Hybrid Artificial Intelligent Systems

Print ISBN: 978-3-319-32033-5

Electronic ISBN: 978-3-319-32034-2

Copyright Year: 2016

DOI
https://doi.org/10.1007/978-3-319-32034-2_7

Premium Partner

    Image Credits