Detecting Common Discussion Topics Across Culture From News Reader Comments

Bei Shi, Wai Lam, Lidong Bing, Yinqing Xu

Abstract: News reader comments found in many on-line news websites are typically massive in amount. We investigate the task of Cultural-common Topic Detection (CTD), which is aimed at discovering common discussion topics from news reader comments written in different languages. We propose a new probabilistic graphical model called MCTA which can cope with the language gap and capture the common semantics in different languages. We also develop a partially collapsed Gibbs sampler which effectively incorporates the term translation relationship into the detection of cultural-common topics for model parameter learning. Experimental results show improvements over the state-of-the-art model.