Polarity Analysis of Editorial Articles towards Fake News Detection

Abstract

The need in verifying online information is essential to identify the lines between fake news and factual information. Social media has become the platform for the digital production of news articles. It can be found from various sources -- blogs to social networking sites, or even online forums. This indicates how potentially fake news can influence the overall opinion of the masses. This study aims to create a model that categorizes online editorial articles and use different classifier to determine its polarity through sentiment analysis. This is a step first taken in order to detect fake against real news online through data mining. In this study, online news articles from various known websites were extracted in order to develop a model. The researcher demonstrate that news articles can be analyzed and showed effective results through the performance of the classifiers used in this study.