Comparison of Multiple Machine Learning Approaches and Sentiment Analysis in Detection of Spam

Currently, all of our communications are made through various electronic communication mediums. While it has made communication between people from different parts of the world very easy, things like spamming have made life difficult for many people. Spammers are found in almost every electronic communication platform like email, mobile SMS, social networking sites, etc. So, with time detecting spam messages and filtering them out from our important messages have become more and more important. For many years, Natural Language Processing (NLP) researchers have proposed different techniques to detect spam messages. In this paper, our objective is to detect spam messages in a dataset using vectorization along with various machine learning algorithms and compare their results to find out the best classifier for detecting spam messages.