Python, ML concepts, NLP concepts
Many online news media outlets rely heavily on the revenue from readers 'clicks and they have to fight for readership because of the existence of multiple media outlets. To persuade viewers to click on an article and then visit the web site, the news reports also come up with enticing headlines following the article links, which inspire viewers to click on the post. Such stories are classified as Clickbait. Although such bait can lure readers into clicking, in the long run, clickbait typically doesn't measure up to the standards of readers, leaving them frustrated.
It is therefore important to categorize articles as clickbait or non-clickbait and alert readers of various media websites of the possibility of these headlines being baited. In this project, we implement two machine learning algorithms ‘Support Vector Machine (SVM)’ and ‘Decision Tree’ to perform this function of classifying the articles and comparing the output of these algorithms. We found that SVM is more accurate in classifying the articles than the decision tree.