Mobile Message Classification Using Natural Language Processing and Machine Learning Algorithms

TL;DR: In this paper , the authors used machine learning algorithms such as Random Forest, KNN, Naïve Bayes, Support Vector Machine, decision tree, and NLP algorithms Count Vectorization and TF-IDF.

Abstract: Abstract: SPAM: Stupid Pointless Annoying Malware is any unwanted, unsolicited digital communication sent out in bulk Although email is the most common method of spreading spam, it can also be communicated via social media, text messages, and phone calls. Sadly, whether we like it or not, spam messages must irritate everyone with a mobile device. Here this project classifies spam messages. Understanding different spam text classification techniques like extraction, text preprocessing, and NLTK stop words is vital. This project mainly focuses on the spam classification approach using machine learning algorithms such as Random Forest, KNN, Naïve Bayes, Support Vector Machine, decision tree, and NLP algorithms Count Vectorization and TF-IDF