Social Media Spam Detection Using NLP in Machine Learning

Many people use social media daily to talk with friends, share their opinions, and stay updated. But one common problem is the presence of spam messages. These messages often bother users and sometimes give false or harmful information. This project helps find and stop spam using Natural Language Processing (NLP) and a method called the Naive Bayes algorithm. It uses a set of social media posts that are already marked as spam or not. The text is first cleaned by breaking it into words, removing useless words, and reducing words to their base form. Then, a method called TF-IDF changes the text into numbers so the computer can understand it better. Once the data is ready, we apply the Naive Bayes method to check whether a message is spam. To see how well the system works, we look at how often it gives correct results and where it makes mistakes. We check this using accuracy and a few other basic methods. Overall, this method works well and can identify spam messages in most situations. Such a system is valuable for social media platforms, as it helps prevent spam from spreading and affecting more users.