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Comparison of Naive Bayes and SVM Classifiers for Detection of Spam SMS using Natural Language Processing


Affiliations
1 Department of Information Technology, Avinashilingam Institute for Home Science and Higher Education for Women, India
2 Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women, India
     

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Day today’s innovative world observers an extraordinary possibility in the communication sector. Individuals will in general utilize various approaches to speak with individuals around the world. The regular methods for sharing short data in an exceptionally simple manner and is cases recorded now a days. This desires a need to recognize Spam SMS to stay away from digital wrongdoing robbery and extortion exercises. A labeled dataset is utilized for recognition reason and two classifiers to be specific Support Vector Machine and Naïve Bayes are utilized to make a correlative examination for the location of spam accomplished by utilizing of Short Message Service. SMS doesn’t require any web charges yet, it is unsurpassed utilized methods for remote correspondence. Each versatile client has this office of course. It has an incredible monetary effect on the clients just as the specialist co-ops. Then again SMS spam is one of the major digital wrong doing SMS and the exhibition of classifiers are thought about.

Keywords

Spam SMS, Support Vector Machine, Naïve Bayes, Classification, Natural Language Processing.
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  • Comparison of Naive Bayes and SVM Classifiers for Detection of Spam SMS using Natural Language Processing

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Authors

N. Krishnaveni
Department of Information Technology, Avinashilingam Institute for Home Science and Higher Education for Women, India
V. Radha
Department of Computer Science, Avinashilingam Institute for Home Science and Higher Education for Women, India

Abstract


Day today’s innovative world observers an extraordinary possibility in the communication sector. Individuals will in general utilize various approaches to speak with individuals around the world. The regular methods for sharing short data in an exceptionally simple manner and is cases recorded now a days. This desires a need to recognize Spam SMS to stay away from digital wrongdoing robbery and extortion exercises. A labeled dataset is utilized for recognition reason and two classifiers to be specific Support Vector Machine and Naïve Bayes are utilized to make a correlative examination for the location of spam accomplished by utilizing of Short Message Service. SMS doesn’t require any web charges yet, it is unsurpassed utilized methods for remote correspondence. Each versatile client has this office of course. It has an incredible monetary effect on the clients just as the specialist co-ops. Then again SMS spam is one of the major digital wrong doing SMS and the exhibition of classifiers are thought about.

Keywords


Spam SMS, Support Vector Machine, Naïve Bayes, Classification, Natural Language Processing.

References