Mining Opinion Words and Opinion Targets in a Two-Stage Framework

This paper proposes a novel two-stage method for mining opinion words and opinion targets. In the first stage, we propose a Sentiment Graph Walking algorithm, which naturally incorporates syntactic patterns in a Sentiment Graph to extract opinion word/target candidates. Then random walking is employed to estimate confidence of candidates, which improves extraction accuracy by considering confidence of patterns. In the second stage, we adopt a self-learning strategy to refine the results from the first stage, especially for filtering out high-frequency noise terms and capturing the long-tail terms, which are not investigated by previous methods. The experimental results on three real world datasets demonstrate the effectiveness of our approach compared with stateof-the-art unsupervised methods.