Genetic algorithm based correlation enhanced prediction of online news popularity

Online News is an article which is meant for spreading awareness of any topic or subject published on the Internet and is available to a large section of users to gather information. For complete knowledge proliferation we need to know the right way and time to do so. For achieving this goal we have come up with a model which on the basis of, multiple factors, like describing the article type (structure and design) and publishing time predicts popularity of the article. In this paper we use Correlation techniques to get the dependency of the popularity obtained from an article, and then we use Genetic Algorithm to get the optimum attributes or best set which should be considered while formatting the article. Data has been procured from UCI Machine Learning Repository with 39644 articles with sixty condition attributes and one decision attribute. We implemented twelve different data learning algorithms on the above mentioned data set, including Correlation Analysis and Neural Network. We have also given a comparison of the performances got from various algorithms in the Result section.