Learning to Align Comments to News Topics

Abstract

With the rapid proliferation of social media, increasingly more people express their opinions and reviews (user-generated content (UGC)) on recent news articles through various online services, such as news portals, forums, discussion groups, and microblogs. Clearly, identifying hot topics that users greatly care about can improve readers’ news browsing experience and facilitate research into interaction analysis between news and UGC. Furthermore, it is of great benefit to public opinion monitoring and management for both industry and government agencies. However, it is extremely time consuming, if not impossible, to manually examine the large amount of available social content. In this article, we formally define the news comment alignment problem and propose a novel framework that: (1) automatically extracts topics from a given news article and its associated comments, (2) identifies and extends positive examples with different degrees of confidence using three methods (i.e., hypersphere, density, and cluster chain), and (3) completes the alignment between news sentences and comments through a weighted-SVM classifier. Extensive experiments show that our proposed framework significantly outperforms state-of-the-art methods.