Weighted cluster-level social emotion classification across domains

Social emotion classification is important for better capturing the preferences and perspectives of individual users to monitor public opinion and edit news. However, news reports have a strong domain dependence. Moreover, training data in the target domain are usually insufficient and only a small amount of training data may be labeled. To address these problems, we develop a cluster-level method for social emotion classification across domains. By discovering both source and target clusters and weighting the cluster in the source domain according to the similarity between its distribution and that of the target cluster, we can discover common patterns between the source and target domains, thus using both source and target data more effectively. Extensive experiments involving 12 cross-domain tasks conducted by using the ChinaNews dataset show that our model outperforms existing methods.