LAUSR

Sina Weibo has become an important data resource for opinion mining. However, this data resource is polluted with un-opinionated posts. Detecting posts containing opinions in Sina Weibo faces two challenges. One is the short text in Sina Weibo that leads to insufficient textual features. The other challenge is the absence of ground-truth data for training models. In this paper, we propose a weakly supervised framework named graph-based opinioned post detector (GOPD) to detect the opinioned posts in Sina Weibo. GOPD utilizes three types of user interactions, which include reposting, responding, and referring, to construct the opinioned similarity graph (OSG) that describes the opinioned similarity between posts. On the OSG, opinioned post detection is formulated as a classification problem. The pairwise Markov random field model and the loopy belief propagation algorithm are employed to solve the problem. GOPD is evaluated on the manually labeled real-world datasets. Results show that the GOPD efficiently detects opinioned posts and transfers cross topics.