Answering Opinion Questions with Random Walks on Graphs

Opinion Question Answering (Opinion QA), which aims to find the authors’ sentimental opinions on a specific target, is more challenging than traditional factbased question answering problems. To extract the opinion oriented answers, we need to consider both topic relevance and opinion sentiment issues. Current solutions to this problem are mostly ad-hoc combinations of question topic information and opinion information. In this paper, we propose an Opinion PageRank model and an Opinion HITS model to fully explore the information from different relations among questions and answers, answers and answers, and topics and opinions. By fully exploiting these relations, the experiment results show that our proposed algorithms outperform several state of the art baselines on benchmark data set. A gain of over 10% in F scores is achieved as compared to many other systems.