In the Fourth Industrial Revolution Era with the fast-growing of multiple types of massive data, digitalized content as well as text-based information, which is increased through social media like Youtube, Facebook,… Many scientists worldwide are deeply interested in data analysis. Many researchers have applied various text classification techniques to improve the Naive Bayes algorithm, particularly due to its tendency for attribute dependence. Resolving this interdependence is key to enhancing the model’s accuracy. The Multinomial Naïve Bayes model is widely used in text classification, where each document is represented by a feature vector of length d, corresponding to the number of words in the dictionary. The manual process of selecting articles is to filter and extract them into lists of documents. But as relevant information is getting more complicated day by day and bulky, together with it is the increasing of fake news, wrong information. That’s why the ability to be able to precisely filter out these online articles is highly important. To ensure whether the readers receive the reliable information or not. That is why we need to develop a precise news selecting system and also get our hands on the predict and filter system to sort out online articles using the strong and well-known Naïve Bayes Algorithm to do the work since it is simple, fast and accurate.