Predicting Social Emotions from ReadersÕ Perspective - 2017


Thanks to the speedy development of.Net, massive numbers of documents assigned by readers’ emotions have been generated through new portals. Comparing to the previous studies that focused on author’s perspective, our research focuses on readers’ emotions invoked by news articles. Our research provides meaningful assistance in social media application such as sentiment retrieval, opinion summarization and election prediction. In this paper, we predict the readers’ emotion of news based mostly on the social opinion network. A lot of specifically, we construct the opinion network primarily based on the semantic distance. The communities in the news network indicate specific events which are connected to the emotions. So, the opinion network is the lexicon between events and corresponding emotions. We tend to leverage neighbor relationship in network to predict readers’ emotions. So, our strategies acquire better result than the state-of-the-art ways. Moreover, we tend to developed a growing strategy to prune the network for practical application. The experiment verifies the rationality of the reduction for application.

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