Accounting for the Correspondence in Commented Data

One important way for people to make their voice heard is to comment on the articles they have read online, such as news reports and each other’s posts. The user-generated comments together with the commented documents form a unique correspondence structure. Properly modeling the dependency in such data is thus vital for one to obtain accurate insight of people’s opinions and attention. In this work, we develop a Commented Correspondence Topic Model to model correspondence in commented text data. We focus on two levels of correspondence. First, to capture topic-level correspondence, we treat the topic assignments in commented documents as the prior to their comments’ topic proportions. This captures the thematic dependency between commented documents and their comments. Second, to capture word-level correspondence, we utilize the Dirichlet compound multinomial distribution to model topics. This captures the word repetition patterns within the commented data. By integrating these two aspects, our model demonstrated encouraging performance in capturing the correspondence structure, which provides improved results in modeling user-generated content, spam comment detection, and sentence-based comment retrieval compared with state-of-the-art topic model solutions for correspondence modeling.