Teenager Friendly News Classification Using Machine Learning Model

Adolescents frequently encounter news reports multiple times daily, which can induce feelings of anxiety, stress, and fear when they come across stories about crimes. Studies indicate that young people tend to replicate behaviors and attitudes they observe in the news, making them vulnerable to becoming numb to violence and increasingly prone to violent and aggressive conduct. Prolonged and repeated exposure to such events may have serious consequences, including fear, insensitivity, and behavioral changes. So, it is essential to have a system that can approximately classify safe and unsafe news for teenagers and only the safe news is visible to them. We address this challenge of text extraction and classification from News Headlines using well-known statistical measures and machine learning (ML) models. In this proposed system, we compare Linear Support Vector Classifier (LSVC), Logistic Regression (LR), Multinomial Naïve Bayes (MNB), Random Forest Classifier (RFC), and Decision Tree Classifier (DTC) algorithms in which LR outperforms the other algorithms.