What’s Popular on the internet? A Machine Learning Approach for News Classification

Authors

  • Arun Vengatesan

Abstract

The extended utilization of the web and the onset of the data technology field has led to a new age where individuals are starting to read news online. Hence, online news has become the fundamental source of information for most individuals, and anticipating the prominence of online news has become a highly important issue that can't be ignored. It could assist authors with introducing serious and highly readable news. Number of shares an article gets, is considered as one of the most obvious factors in determining its popularity. In this paper, we apply distinctive machine learning methods to anticipate the quantity of shares and categorize them as well known and unpopular. The information has been assembled from Mashable, a notable online news gateway. Methods such as linear regression and classification techniques like decision tree, SVM, and logistic regression are utilized to analyse the data set. The performance of these methods is measured by their accuracy, precision, and recall measures. The findings of this research work can be utilized by several online news agencies to anticipate their popularity based on their content and make changes accordingly. This will also help the news agencies in adopting promising marketing and advertising strategies.

Published

2020-12-01

Issue

Section

Articles