EPNDR: Emotion Prediction For News Documents Based On Readers’ Perspectives

Due to the rapid rise in Internet population, the content over the web is increasing and a large number of documents assigned by reader’s emotions have been generated through new portals. Earlier works have focused only on author’s perspective, this work focuses on reader’s emotions generated by news articles. In this work, Emotion Prediction for News Documents based on Readers’ 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 reader’s emotions are much better when compared with the existing method Opinion Network Community (ONC) [1].

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