Design and Analysis of News Category Predictor

Abstract

Recent technological advancements have changed significantly the way news is produced, consumed, and disseminated. Frequent and on-spot news reporting has been enabled, which smartphones can access anywhere and anytime. News categorization or classification can significantly help in its proper and timely dissemination. This study evaluates and compares news category predictors' performance based on four supervised machine learning models. We choose a standard dataset of British Broadcasting Corporation (BBC) news consisting of five categories: business, sports, technology, politics, and entertainment. Four multi-class news category predictors have been developed and trained on the same dataset: Naïve Bayes, Random Forest, K-Nearest Neighbors (KNN), and Support Vector Machine (SVM). Each category predictor's performance was evaluated by analyzing the confusion matrix and quantifying the test dataset's precision, recall, and overall accuracy. In the end, the performance of all category predictors was studied and compared. The results show that all category predictors have achieved satisfactory accuracy grades. However, the SVM model performed better than the four supervised learning models, categorizing news articles with 98.3% accuracy. In contrast, the lowest accuracy was obtained by the KNN model. However, the KNN model's performance can be enhanced by investigating the optimal number of neighbors (K) value.