Cyberbullying can be visualized as a potential issue affecting children and all categories of people. One demanding concern is effective representation for learning of content messages. The proposed system deals with cyberbullying revelation in email application using Naive Bayes Classifier Algorithm. The Classification Algorithm is a baseline method for content classification; the method of analyzing documents as relating to one classification or the other with word prevalence as features. The technique deals with the identification and filtering of spam words. The denoised messages are classified with the help of Naive Bayes Classifier Algorithm. The messages are processed under feature set extraction method. The feature probabilities are found out using Naive Bayes Classifier Algorithm .The efficiency factor is compared among the two algorithms, Naive Bayes Classifier Algorithm and Support Vector Machine and a graph is plotted. Comparison on the basis of precision factor is also done with the fact that the probabilities for each feature set are calculated independently from the twitter dataset and can evaluate the performance by predicting the output variable.

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