Due to the rapid rise in Internet population, the content over the web is increasing and a large number of documents assigned by readerâ€™s emotions have been generated through new portals. Earlier works have focused only on authorâ€™s perspective, this work focuses on readerâ€™s emotions generated by news articles. In this work, Emotion Prediction for News Documents based on Readersâ€™ Perspectives (EPNDR) is proposed More specifically, we form four communities based on the higest ratings that are present in the news articles. Further, a textual relevance is computed based on the word frequency for a particular document and insert all the remaining articles to the four communities. When a new document arrives, the probability of the new document being near to all the documents in a community is found. The emotion rating for the new document is predicted using nearest neighbour analysis. Experiments are conducted on the news articles and as a result, it is observed that the proposed method results in predicting readerâ€™s emotions are much better when compared with the existing method Opinion Network Community (ONC) .