Online commenting to news articles provides a communication channel between media professionals and readers offering a crucial tool for opinion exchange and freedom of expression. Currently, comments are detached from the news article and thus removed from the context that they were written for. In this work, we propose a method to connect readers’ comments to the news article segments they refer to. We use similarity features to link comments to relevant article segments and evaluate both word-based and term-based vector spaces. Our results are comparable to state-of-theart topic modeling techniques when used for linking tasks. We demonstrate that article segments and comments representation are relevant to linking accuracy since we achieve better performances when similarity features are computed using similarity between terms rather than words.
['Ahmet Aker', 'Emina Kurtic', 'Mark Hepple', 'Robert J. Gaizauskas', 'Giuseppe Di Fabbrizio']
Comment-to-Article Linking in the Online News Domain