A novel approach to spam filtering using semantic based naive bayesian classifier in text analytics

Abstract

Nowadays, big data analytics has taken a major role in different fields. In this way, text analytics is a challenging field which is a part of big data analytics. It is the process of converting understand text data into meaningful data for analysis to provide sentiment analysis. The main role of text analytics is to extract semantic meaning from contents of different text and to classify this text. We use text analytics in the field of spam filtering so that we can analyze text which is incorporated within spam mails. In the Internet era, as the number of email users is increased in exponential rate, spam filtering is a major issue for text classification. We propose semantic based Naïve Bayesian classification algorithm in spam filtering. We use R language which is open source and statistical analysis language, by which we can analyze spam mails for text analytics by calculating the accuracy of different subjects of spam mail.