Predicting Social Emotions based on Textual Relevance for News Documents

Due to the rapid rise in internet population, the content over 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 author's perspective, our work focuses on reader's emotions generated by news articles. Social emotions of news articles from reader's perspective are predicted with the help of user ratings. More specifically, we form Communities based on the ratings that are present in the news articles. Further, a Textual Relevance is computed based on the word frequency for a particular document. 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].