Contentious news issues, such as the health care reform debate, draw much interest from the public; however, it is not simple for an ordinary user to search and contrast the opposing arguments and have a comprehensive understanding of the issues. Providing a classified view of the opposing views of the issues can help readers easily understand the issue from multiple perspectives. We present a disputant relation-based method for classifying news articles on contentious issues. We observe that the disputants of a contention are an important feature for understanding the discourse. It performs unsupervised classification on news articles based on disputant relations, and helps readers intuitively view the articles through the opponent-based frame and attain balanced understanding, free from a specific biased viewpoint. The method is performed in three stages: disputant extraction, disputant partitioning, and article classification. We apply a modified version of HITS algorithm and an SVM classifier trained with pseudorelevant data for article analysis. We conduct an accuracy analysis and an upper-bound analysis for the evaluation of the method.

This paper explores the incorporation of topical support documents into a training set as a means of compensating for a shortage of positive training data in text categorization. To support topical representation, our method applies a simple transformation to documents, i.e., making new documents from existing positive documents by squaring a conventional term weight. The topical support documents thus created not only are expected to preserve the topic, but even improve the topical representation by emphasizing terms with higher weights. Experiments with support vector machines showed the effectiveness on RCV1 collection with a small number of positive training data. Our topical support representation achieved 52.01% and 8.83% improvements for 33 and 56 categories of RCV1 Topic in micro-averaged F1 with less than 100 and 300 positive documents in learning, respectively. Result analyses based on robustness indicate that topical support documents contribute to a steady and stable improvement.