Machine Learning-Based Models :learn patterns from labeled data, making predictions without explicit programming. They rely on manual feature extraction, including linguistic, theme, user, and communication features, to improve classification accuracy.
Random Forest : operates by constructing multiple decision trees during training and outputting the mode of the classes (classification) or the average prediction (regression) of the individual trees.
Naive Bayes : simple probabilistic classifier based on Bayes theorem with the naive assumption of feature independence.
Support Vector Classifier : aims to maximize the margin between classes while minimizing classification errors, making it effective for both linear and non-linear classification tasks.