EANRS: An Emotional Arabic News Recommender System

In “News field”, the user emotion plays significant roles in the decision making Process. The main objective of this paper is offering news to users according to their preferences, and to making positive news items that can have positive impact on users’ mind and soul. Implementation of an emotional Arabic news recommender system (E-ANRS) application to display news articles for our users and get users’ feedbacks including two categories as like/dislike via using android platform. We introduce our model as a solution for two problems as follows: the first problem is design a model which suggests positive Arabic news to excel the violence and vehemence people are shown in everyday life, a resource for recommending Arabic news with extract of emotion is unprecedented. The second problem, we introduce an (E-ANRS) as a solution to problem of cold-start by using IBM Bluemix server for first time, which provides two services (language translator and tone analyzer). We have two ways to measure accuracy of emotion for our model by using EEG and SAM techniques. The obtained result of EEG of our model is 90%. We compared performance of our proposed model with other studies and the results proved that our model offers a better and perfect recommendation process and emotion extracted performance greatly improved with the use of news texts in Arabic languages.