Abstract
Spam is a universal problem with which everyone is familiar. A number of approaches are used for Spam filtering. The most common filtering technique is content-based filtering which uses the actual text of message to determine whether it is Spam or not. The content is very dynamic and it is very challenging to represent all information in a mathematical model of classification. For instance, in content-based Spam filtering, the characteristics used by the filter to identify Spam message are constantly changing over time. Na?ve Bayes method represents the changing nature of message using probability theory and support vector machine (SVM) represents those using different features. These two methods of classification are efficient in different domains and the case of Nepali SMS or Text classification has not yet been in consideration; these two methods do not consider the issue and it is interesting to find out the performance of both the methods in the problem of Nepali Text classification. In this paper, the Na?ve Bayes and SVM-based classification techniques are implemented to classify the Nepali SMS as Spam and non-Spam. An empirical analysis for various text cases has been done to evaluate accuracy measure of the classification methodologies used in this study. And, it is found to be 87.15% accurate in SVM and 92.74% accurate in the case of Na?ve Bayes.
Highlights
Spam can be defined as unsolicited email for a recipient or any email that the users do not wanted to have in their inboxes
Naïve Bayes and Support Vector Machine algorithms have been implemented for the Spam filtering task
The study has gone through the empirical analysis of the performance of both the Spam filters (SVM and Naïve Bayes) for Nepali SMS
Summary
Spam can be defined as unsolicited (unwanted, junk) email for a recipient or any email that the users do not wanted to have in their inboxes. Spam filtering is a special problem in the field of document classification and machine learning. The technological development in mobile devices has increased in computational power, and other powerful systems have been capable to be connected to mobile phone networks. This has increased the communication through SMS. A mail consists of certain structured information such as subject, mail header, salutation, sender’s address etc. These make the SMS classification task much difficult. This situation makes the necessity for developing an efficient SMS filtering method.
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