Early development of Web, large numbers of documents assigned by readers’ emotions have been generated through new portals. By analyzing to the previous studies which focused on author’s perspective, our research focuses on readers’ emotions invoked by news articles. Our research paper 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, social media based on the social opinion network. Most specifically, we construct the opinion network based on the semantic distance. The communities in the news network, opinion network indicate specific events which are related to the emotions. Therefore, the news network, opinion network serves as the lexicon between events and corresponding emotions. We discussed neighbor relationship in network to predict readers’ emotions. At the last our result, our methods obtain better result than the state-of-the-art methods. Moreover, our research developed a growing strategy to prune the network for practical application. The experiment shows the rationality of the reduction for application.
sensing and analysis, opinion network, text mining, complex network
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