News Classification from Microblogging Dataset using Supervised Learning

Abstract

Today, Microblog is one of the most popular social networks. There is a lot of data floating on this microblog that is useful for both general and unique purposes (e.g. business, current trend). From here, people can also get different types of news. We may use a supervised machine learning algorithm in order to reduce the search effort. One of the basic tasks for categorizing news is classification. We have proposed a model in this paper to identify news from the Twitter dataset and find the best outcome for the microblogging dataset. This task began with basic data crawling and after applying four supervised learning algorithms, ended with the selection of the best one. Eventually, we chose our best template for the crawled dataset.