An intelligent spam detection using text mining

It enginnering

With the growing usages of social media application has main part of our daily life routine. According to oxford dictionary the spam is unsolicited and inapplicable messages send to the internet for large number of peoples. The motivation of spam is to spread the malware and pronunciation. The problem of spam becomes very critical for internet community. Sometimes large amount of web pages & social websites are considered as spam. Many spam techniques were proposed to approach the problem of spamming. The problem is direct affected to the users of the social networking sites such as Gmail, YouTube, Twitter, Facebook, Google+. The social networking sites Facebook and twitter are most familiar to the users. People used twitter in daily life and this reason why spammer most of attack on this social networking sites. In this research with the use of OSN we can classify data into two forms spam and Non spam .We first collect data from OSN application such as twitter by downloading comments. By using different algorithms and different techniques we further check whether the data is spam or non spam. We have developed a twitter based spam detection system using twitter comments and URLs. Our system comprises a database as training data set of comments & recognition will be applied on same comments for spam detection. The spam detection shows to drive people off of twitter and onto another social networking site, it will likely violate our spam policies. So our main aim is to develop spam detection system taking "spammer do not take advantage" into mind.

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.