Cross-Domain and Cross-Category Emotion Tagging for Comments of Online News

In many online news services, users often write comments towards news in subjective emotions such as sadness, happiness or anger. Knowing such emotions can help understand the preferences and perspectives of individual users, and therefore may facilitate online publishers to provide more relevant services to users. Although building emotion classifiers is a practical task, it highly depends on sufficient training data that is not easy to be collected directly and the manually labeling work of comments can be quite labor intensive. Also, online news has different domains, which makes the problem even harder as different word distributions of the domains require different classifiers with corresponding distinct training data. This paper addresses the task of emotion tagging for comments of cross-domain online news. The cross-domain task is formulated as a transfer learning problem which utilizes a small amount of labeled data from a target news domain and abundant labeled data from a different source domain. This paper proposes a novel framework to transfer knowledge across different news domains. More specifically, different approaches have been proposed when the two domains share the same set of emotion categories or use different categories. An extensive set of experimental results on four datasets from popular online news services demonstrates the effectiveness of our proposed models in cross-domain emotion tagging for comments of online news in both the scenarios of sharing the same emotion categories or having different categories in the source and target domains